In [32]:
plot(knnmodel)
#Loading the dataset
data <- read.csv('WA_Fn-UseC_-HR-Employee-Attrition.csv')
options(repr.matrix.max.cols=150, repr.matrix.max.rows=200)
library(pacman)
MYLIBRARIES<-c("caret",
"dplyr",
"data.table",
"splitTools",
"parallel",
"parallelMap",
"xgboost",
'DiagrammeR',
'rpart',
'tree',
'rpart.plot',
'parallelMap',
'parallel',
"performanceEstimation")
pacman::p_load(char=MYLIBRARIES, install=TRUE, character.only = TRUE)
# create new dataset from minus columns in the subset
df <- subset(data, select = -c(StandardHours, Over18 , EmployeeCount , EmployeeNumber,DailyRate,MonthlyRate) )
cols <- c("JobRole", "OverTime", "Department", "MaritalStatus",'Gender', 'Attrition' , 'EducationField','Education'
,'EnvironmentSatisfaction','JobLevel', 'JobInvolvement','JobSatisfaction','RelationshipSatisfaction',
'StockOptionLevel','TrainingTimesLastYear','WorkLifeBalance')
#factor encode columns
df[cols] <- lapply(df[cols], factor)
#create new features work experience score and Attrition Score
df <- df %>% mutate(WorkExperienceScore = TotalWorkingYears/Age)
df <- df%>% mutate(AttritionScore = NumCompaniesWorked / (TotalWorkingYears + 1))
#round new columns work experience score and Attrition score to two decimal places
df$WorkExperienceScore = round(df$WorkExperienceScore, digits = 2)
df$AttritionScore =round(df$AttritionScore, digits = 2)
#Create new feature ManagersYearsLabel based on condition from years under current manager
df$ManagerYearsLabel <-
ifelse(
df$YearsWithCurrManager <= 1,
'New Hire',
ifelse(
df$YearsWithCurrManager > 1 &
df$YearsWithCurrManager <= 4,
'Experenced Hire',
'Verteran Hire'
)
)
#Remove more features no longer needed
df <- subset(df, select = -c(TotalWorkingYears , Age,NumCompaniesWorked ,YearsWithCurrManager) )
#Convert business travel to a factor with level 1,2,3
df$BusinessTravel <- factor(df$BusinessTravel,
levels = c('Travel_Frequently', 'Travel_Rarely', 'Non-Travel'),
labels = c(1, 2, 3))
#Convert manager label to factor
df$ManagerYearsLabel <- as.factor(df$ManagerYearsLabel)
#set seed for dataset and randomise dataset
set.seed(11)
df<-df[order(runif(nrow(df))),]
#train test splits using partition function (70 percent train , 30 percent test)
inds <- partition(df$Attrition, p = c(train = 0.7,test = 0.3))
train <- df[inds$train, ]
test <- df[inds$test, ]
#Smote oversampling on dataset
train_smote <- smote(Attrition ~ . , train, perc.over = 2, k = 3, perc.under = 1)
#write.csv(train,"../Desktop/train.preprocessed", row.names = FALSE)
#write.csv(test,"../Desktop/test.preprocessed", row.names = FALSE)
#Function to calculate accuracy of each model
model.accuracy <- function(table) {
acc <- round((table[1,1] + table[2,2] ) / (table[1,1] + table[1,2] +table[2,1] + table[2,2]) , digits=2)
return(acc)
}
#Function to calculate recall for yes cases of attrition
AttritionYes.recall <- function(table){
rec <- round((table[2,2] ) / (table[2,2]+table[2,1]),digits=2)
return(rec)
}
#Function to calculate precision for yes cases of attrition
AttritionYes.precision <- function(table){
prec <- round((table[2,2] ) / (table[2,2]+ table[1,2]) ,digits=2)
return(prec)
}
#Function to calculate recall for no cases of attrition
AttritionNo.recall <- function(table){
rec <- round((table[1,1] ) / (table[1,1]+table[2,1]),digits=2)
return(rec)
}
#Function to calculate precision for no cases of attrition
AttritionNo.precision <- function(table){
prec <- round((table[1,1] ) / (table[1,1]+ table[1,2]) ,digits=2)
return(prec)
}
#Function to calculate total precision of model
total.precision <- function(table){
prec <- round(((table[2,2] ) / (table[2,2]+ table[1,2])
+ (table[1,1] ) / (table[1,1]+ table[1,2]) ) / 2 , digits=2)
return(prec)
}
#Function to calculate total recall of model
total.recall <- function(table){
prec <- round(((table[1,1] ) / (table[1,1]+ table[2,1])
+ (table[2,2] ) / (table[2,2]+ table[2,1]) ) / 2 , digits=2)
return(prec)
}
#One hot encoding dataset using dummyVars
dummy <- dummyVars(" ~ .", data=df)
knndf <- data.frame(predict(dummy, newdata = df))
# remove attrition.no from one hot encoded dataset
knndf <- subset(knndf, select = -c(Attrition.No) )
#change name of Atrrition.Yes to Attrition
names(knndf)[names(knndf) == "Attrition.Yes"] <- "Attrition"
# Set Attrition value of 0 to No
#Set Attrition value of 1 to Yes
knndf["Attrition"][knndf["Attrition"] == 0] <- 'No'
knndf["Attrition"][knndf["Attrition"] == 1] <- 'Yes'
#Factor Encode our target label attrition
knndf$Attrition <- as.factor(knndf$Attrition)
#Train Test splits to be used for knn modelling
knninds <- partition(knndf$Attrition, p = c(train = 0.7, test = 0.3))
knntrain <- knndf[knninds$train, ]
knntest <- knndf[knninds$test, ]
#Smote oversampling applied to knn dataset used to train our data
knn.train_smote <- smote(Attrition ~ . , knntrain, perc.over = 2, k = 5, perc.under = 1)
#method = repeatedcv - Sets resampling method to repeated cross validation
# Number = 10 - Sets cross validation fold to 10
#repeats = 3 - Sets 10-fold cross-validations to reapeat 3 times
x = trainControl(method = 'repeatedcv',number = 10,repeats = 3)
knnmodel <- train(Attrition~. , data = knntrain, method = 'knn',#train knndata with method as knn and Target label Attrition
preProcess = c('center','scale'), # center and scale dataset to mean 0 and standard deviation 1
trControl = x, #Train control method set to values previously stated(x)
tuneGrid = expand.grid(k = 1:20),# Grid Search for k values 1 to 20
metric = 'Accuracy', # Sets metric for training model to Accuracy
tuneLength = 10) #Sets the number of different values to try for each tunning parameter.
plot(knnmodel)
#predict model using test set
knnPredict <- predict(knnmodel , newdata = knntest )
#customize width and height of plot
options(repr.plot.width=8, repr.plot.height=6)
#store prediction results in a table
conf_df <- data.frame(table(knntest$Attrition, knnPredict))
#plotting of confusion matrix using ggplot2 library
ggplot(data = conf_df, mapping = aes(x = knnPredict, y = Var1)) +
geom_tile(aes(fill = Freq), colour = "white") +
geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1) +
scale_fill_gradient(low = "#F3F781", high = "green") + theme(legend.position="none", strip.background = element_blank(), strip.text.x = element_blank(),
plot.title=element_text(hjust=0.5, color="white"), plot.subtitle=element_text(colour="white"), plot.background=element_rect(fill="#0D7680"),
axis.text.x=element_text(colour="white"), axis.text.y=element_text(colour="white"),
axis.title=element_text(colour="white"),
legend.background = element_rect(fill="#FFF9F5",
size=0.5, linetype="solid",
colour ="black")) +
labs(title="Knn Hypertuned Confusion Matrix ", y="Attrition Actual", x="Predicted Atrriction")
#store table of predicted and actual values
cm.knn<- table(knntest$Attrition, knnPredict)
#apply accuracy formula on table
knn.hypertunedaccuracy <- model.accuracy(cm.knn)
#apply recall formula on yes cases of attrtiion
knn.hypertunedrecallYes <- AttritionYes.recall(cm.knn)
#apply precision formula on yes cases of attrtiion
knn.hypertunedprecisionYes <- AttritionYes.precision(cm.knn)
#calculate F1 Score for yes cases of attrition and round to two decimal places
knn.hypertuned_F1ScoreYes <- round((2 * knn.hypertunedprecisionYes * knn.hypertunedrecallYes) / (knn.hypertunedrecallYes
+knn.hypertunedprecisionYes) ,digits=2)
#apply recall formula on no cases of attrtiion
knn.hypertunedrecallNo <- AttritionNo.recall(cm.knn)
#apply precision formula on no cases of attrtiion
knn.hypertunedprecisionNo <- AttritionNo.precision(cm.knn)
#calculate F1 Score for no cases of attrition and round to two decimal places
knn.hypertuned_F1ScoreNo <- round((2 * knn.hypertunedprecisionNo * knn.hypertunedrecallNo) / (knn.hypertunedrecallNo
+knn.hypertunedprecisionNo) ,digits=2)
#apply total precision formula(both cases of attrtiion)
total.precision.hypertuned_knn <- total.precision(cm.knn)
#apply total recall formula (both cases of attrtiion)
total.recall.hypertuned_knn <- total.recall(cm.knn)
#calculate total F1 score (both cases of attrtiion)
total.F1Scoreknn<- round((knn.hypertuned_F1ScoreNo + knn.hypertuned_F1ScoreYes)/2,digits=2)
smote.knnmodel <- train(Attrition~. , data = knn.train_smote, method = 'knn',#train knndata with method as knn and Target label Attrition
preProcess = c('center','scale'), #center and scale dataset to mean 0 and standard deviation 1
trControl = x, #Train control method set to values previously stated(x)
tuneGrid = expand.grid(k = 1:20),
metric = 'Accuracy', # Grid Search for k values 1 to 20
tuneLength = 10) #Sets the number of different values to try for each tunning parameter.
plot(smote.knnmodel)
#predict model using test set
smote.knnPredict <- predict(smote.knnmodel , newdata = knntest )
#customize width and height of plot
options(repr.plot.width=8, repr.plot.height=6)
#store prediction results in a table
conf_df <- data.frame(table(knntest$Attrition, smote.knnPredict))
#plotting of confusion matrix using ggplot2 library
ggplot(data = conf_df, mapping = aes(x = smote.knnPredict, y = Var1)) +
geom_tile(aes(fill = Freq), colour = "white") +
geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1) +
scale_fill_gradient(low = "#F3F781", high = "green") + theme(legend.position="none", strip.background = element_blank(), strip.text.x = element_blank(),
plot.title=element_text(hjust=0.5, color="white"), plot.subtitle=element_text(colour="white"), plot.background=element_rect(fill="#0D7680"),
axis.text.x=element_text(colour="white"), axis.text.y=element_text(colour="white"),
axis.title=element_text(colour="white"),
legend.background = element_rect(fill="#FFF9F5",
size=0.5, linetype="solid",
colour ="black")) +
labs(title="Smoted Knn Hypertuned Confusion Matrix ", y="Attrition Actual", x="Predicted Atrriction")
#store table of predicted and actual values
cm.smoteknn<- table(knntest$Attrition, smote.knnPredict)
#apply accuracy formula on oversampled table
smote.knn.hypertunedaccuracy <- model.accuracy(cm.smoteknn)
#apply recall formula on yes cases of attrtiion
smote.knn.hypertunedrecallYes <- AttritionYes.recall(cm.smoteknn)
#apply precision formula on yes cases of attrtiion
smote.knn.hypertunedprecisionYes <- AttritionYes.precision(cm.smoteknn)
##calculate F1 Score for yes cases of attrtiion
smote.knn.hypertuned_F1ScoreYes <- round((2 * smote.knn.hypertunedprecisionYes * smote.knn.hypertunedrecallYes) / (smote.knn.hypertunedrecallYes
+smote.knn.hypertunedprecisionYes) ,digits=2)
#apply recall formula on no cases of attrtiion
smote.knn.hypertunedrecallNo <- AttritionNo.recall(cm.smoteknn)
#apply precision formula on no cases of attrtiion
smote.knn.hypertunedprecisionNo <- AttritionNo.precision(cm.smoteknn)
##calculate F1 Score for yes cases of attrtiion
smote.knn.hypertuned_F1ScoreNo <- round((2 * smote.knn.hypertunedprecisionNo * smote.knn.hypertunedrecallNo) / (smote.knn.hypertunedrecallNo
+smote.knn.hypertunedprecisionNo) ,digits=2)
#apply total recall formula (both cases of attrtiion)
total.recall.smote_knn <- total.recall(cm.smoteknn)
#apply total precision formula (both cases of attrtiion)
total.precision.smote_knn <- total.precision(cm.smoteknn)
#calculate total F1 Score(both cases of attrtiion)
total.F1Score.smote_knn<- round((smote.knn.hypertuned_F1ScoreNo + smote.knn.hypertuned_F1ScoreYes)/2,digits=2)
#Base decision tree cp set to 0 to show all trees
basetree <- rpart(
Attrition ~. ,
data = train,
method = "class" , control = rpart.control(cp = 0)
)
options(repr.plot.width=10, repr.plot.height=8) #customize width and height of plot
rpart.plot(basetree) #plot tree using rpart.plot library
#predict model using test set
base.preds <- predict(basetree, test, type="class")
#store table of predicted and actual values
cmdecisiontree.base <- table(test$Attrition, base.preds)
#apply accuracy formula on table
dectree.baseaccuracy <- model.accuracy(cmdecisiontree.base)
#apply recall formula on yes cases of attrtiion
dectree.baserecallYes <- AttritionYes.recall(cmdecisiontree.base)
#apply precision formula on yes cases of attrtiion
dectree.baseprecisionYes <- AttritionYes.precision(cmdecisiontree.base)
#calculate F1 Score for yes cases of attrtiion
dectree.base_F1ScoreYes <- round((2 * dectree.baseprecisionYes * dectree.baserecallYes) / (dectree.baserecallYes
+dectree.baseprecisionYes) ,digits=2)
#apply recall formula on no cases of attrtiion
dectree.baserecallNo <- AttritionNo.recall(cmdecisiontree.base)
#apply precision formula on no cases of attrtiion
dectree.baseprecisionNo <- AttritionNo.precision(cmdecisiontree.base)
#calculate F1 Score for no cases of attrtiion
dectree.base_F1ScoreNo <- round((2 * dectree.baseprecisionNo * dectree.baserecallNo) / (dectree.baserecallNo
+dectree.baseprecisionNo) ,digits=2)
#apply total recall formula (both cases of attrtiion)
total.recall.basetree <- total.recall(cmdecisiontree.base)
#apply total precision formula (both cases of attrtiion)
total.precision.basetree <- total.precision(cmdecisiontree.base)
#calculate total F1 Score(both cases of attrtiion)
total.F1Score.basetree<- round((dectree.base_F1ScoreNo + dectree.base_F1ScoreYes)/2,digits=2)
#Oversampled decision tree cp set to 0 to show all trees
smotebasetree <- rpart(
Attrition ~. ,
data = train_smote,
method = "class" , control = rpart.control(cp = 0)
)
options(repr.plot.width=10, repr.plot.height=8) #customize width and height of plot
rpart.plot(smotebasetree) #plot tree using rpart.plot library
#predict model using test set
smotebase.preds <- predict(smotebasetree, test, type="class")
#store table of predicted and actual values
cm.smotedecisiontree.base <- table(test$Attrition, smotebase.preds)
#apply accuracy formula on table
smote.dectree.baseaccuracy <- model.accuracy(cm.smotedecisiontree.base)
#apply recall formula on yes cases of attrtiion
smote.dectree.baserecallYes <- AttritionYes.recall(cm.smotedecisiontree.base)
#apply precision formula on yes cases of attrtiion
smote.dectree.baseprecisionYes <- AttritionYes.precision(cm.smotedecisiontree.base)
# calculate F1 Score for yes cases of attrtiion
smote.dectree.base_F1ScoreYes <- round((2 * smote.dectree.baseprecisionYes * smote.dectree.baserecallYes) / (smote.dectree.baserecallYes
+smote.dectree.baseprecisionYes) ,digits=2)
#apply recall formula on no cases of attrtiion
smote.dectree.baserecallNo <- AttritionNo.recall(cm.smotedecisiontree.base)
#apply precision formula on yes cases of attrtiion
smote.dectree.baseprecisionNo <- AttritionNo.precision(cm.smotedecisiontree.base)
# calculate F1 Score for no cases of attrtiion
smote.dectree.base_F1ScoreNo <- round((2 * smote.dectree.baseprecisionNo * smote.dectree.baserecallNo) / (smote.dectree.baserecallNo
+smote.dectree.baseprecisionNo) ,digits=2)
total.recall.smote_basetree <- total.recall(cm.smotedecisiontree.base) #apply total recall formula (both cases of attrtiion)
total.precision.smote_basetree <- total.precision(cm.smotedecisiontree.base) #apply total precision formula (both cases of attrtiion)
total.F1Score.smote_basetree<- round((smote.dectree.base_F1ScoreNo + smote.dectree.base_F1ScoreYes)/2,digits=2) #calulate combined F1 score (both cases of attrtiion)
#load mlr using pacman .Note(has to be loaded here as it interfers with some other packages)
pacman::p_load(char='mlr', install=TRUE, character.only = TRUE)
tune.model<- makeClassifTask(data = train, target = "Attrition" ) # Convert training data to a classifcation task and store as new variable
tune.tree <- makeLearner("classif.rpart") # Sets learner for our classifcation task to rpart(decision tree)
#define hypertune parameters to be used
treeParamSpace <- makeParamSet(
makeIntegerParam("minsplit", lower = 0, upper = 10),# Minimum Split ranging from 0 to 10
makeIntegerParam("minbucket", lower = 0, upper = 10), # Minimum Bucket ranging from 0 to 10
makeNumericParam("cp", lower = 0.01, upper = 0.1), #Complexity Parameter(cp) ranging from 0.01 to 0.1
makeIntegerParam("maxdepth", lower = 3, upper =5)) #maximum tree depth ranging from 3 to 5
randSearch <- makeTuneControlRandom(maxit = 20) # sets the maximum iterations used for random search
cvForTuning <- makeResampleDesc("CV", iters = 10) # sets resampling method to a 10 fold cross validation
#training task hypertuned/cross validated and ran in parallel
tunedTreePars <- tuneParams(tune.tree, task = tune.model, #classification task using decision tree
resampling = cvForTuning, # resampling strategy to be used based on previously defined input
par.set = treeParamSpace, # Parameter set to be used based on previously defined input
control = randSearch) #control strategy to be used based on previously defined input
parallelStop() # stop parallel run
[Tune] Started tuning learner classif.rpart for parameter set:
Type len Def Constr Req Tunable Trafo
minsplit integer - - 0 to 10 - TRUE -
minbucket integer - - 0 to 10 - TRUE -
cp numeric - - 0.01 to 0.1 - TRUE -
maxdepth integer - - 3 to 5 - TRUE -
With control class: TuneControlRandom
Imputation value: 1
[Tune-x] Setting hyperpars failed: Error in setHyperPars2.Learner(learner, insert(par.vals, args)) :
0 is not feasible for parameter 'minsplit'!
[Tune-x] 1: minsplit=0; minbucket=5; cp=0.0753; maxdepth=4
[Tune-y] 1: mmce.test.mean= NA; time: 0.0 min
[Tune-x] 2: minsplit=5; minbucket=5; cp=0.0113; maxdepth=5
[Tune-y] 2: mmce.test.mean=0.1556825; time: 0.0 min
[Tune-x] 3: minsplit=6; minbucket=7; cp=0.0753; maxdepth=5
[Tune-y] 3: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] 4: minsplit=2; minbucket=8; cp=0.0744; maxdepth=3
[Tune-y] 4: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] 5: minsplit=8; minbucket=9; cp=0.0874; maxdepth=4
[Tune-y] 5: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] 6: minsplit=4; minbucket=4; cp=0.0503; maxdepth=5
[Tune-y] 6: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] 7: minsplit=8; minbucket=5; cp=0.0959; maxdepth=5
[Tune-y] 7: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] 8: minsplit=8; minbucket=4; cp=0.0995; maxdepth=5
[Tune-y] 8: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] Setting hyperpars failed: Error in setHyperPars2.Learner(learner, insert(par.vals, args)) :
0 is not feasible for parameter 'minbucket'!
[Tune-x] 9: minsplit=7; minbucket=0; cp=0.0816; maxdepth=3
[Tune-y] 9: mmce.test.mean= NA; time: 0.0 min
[Tune-x] 10: minsplit=6; minbucket=4; cp=0.0589; maxdepth=4
[Tune-y] 10: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] 11: minsplit=9; minbucket=8; cp=0.0493; maxdepth=5
[Tune-y] 11: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] 12: minsplit=3; minbucket=9; cp=0.0289; maxdepth=4
[Tune-y] 12: mmce.test.mean=0.1615077; time: 0.0 min
[Tune-x] 13: minsplit=10; minbucket=4; cp=0.0685; maxdepth=4
[Tune-y] 13: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] 14: minsplit=6; minbucket=8; cp=0.0252; maxdepth=3
[Tune-y] 14: mmce.test.mean=0.1585951; time: 0.0 min
[Tune-x] 15: minsplit=1; minbucket=5; cp=0.0831; maxdepth=5
[Tune-y] 15: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] 16: minsplit=4; minbucket=5; cp=0.0391; maxdepth=4
[Tune-y] 16: mmce.test.mean=0.1644108; time: 0.0 min
[Tune-x] 17: minsplit=2; minbucket=8; cp=0.0236; maxdepth=5
[Tune-y] 17: mmce.test.mean=0.1624881; time: 0.0 min
[Tune-x] Setting hyperpars failed: Error in setHyperPars2.Learner(learner, insert(par.vals, args)) :
0 is not feasible for parameter 'minsplit'!
[Tune-x] 18: minsplit=0; minbucket=1; cp=0.0991; maxdepth=4
[Tune-y] 18: mmce.test.mean= NA; time: 0.0 min
[Tune-x] 19: minsplit=8; minbucket=9; cp=0.0447; maxdepth=5
[Tune-y] 19: mmce.test.mean=0.1605178; time: 0.0 min
[Tune-x] Setting hyperpars failed: Error in setHyperPars2.Learner(learner, insert(par.vals, args)) :
0 is not feasible for parameter 'minbucket'!
[Tune-x] 20: minsplit=5; minbucket=0; cp=0.0946; maxdepth=5
[Tune-y] 20: mmce.test.mean= NA; time: 0.0 min
[Tune] Result: minsplit=5; minbucket=5; cp=0.0113; maxdepth=5 : mmce.test.mean=0.1556825
tunedTree <- setHyperPars(tune.tree, par.vals = tunedTreePars$x) # set hyper parameters to best parameters from hypertuning/cross validation
tunedTreeModel <- train(tunedTree, tune.model) # train model using those parameters
treeModelData <- getLearnerModel(tunedTreeModel) # Get underlying R model of learner integrated into mlr
options(repr.plot.width=10, repr.plot.height=15) #customize width and height of plot
rpart.plot(treeModelData, roundint = FALSE, ##plot tree using rpart.plot library
box.palette = "BuBn",
type = 5)
#predict model using test set
hypertunetree.preds<- predict(treeModelData, test, type="class")
#store table of predicted and actual values
cmdecisiontree.tuned <- table(test$Attrition, hypertunetree.preds)
#apply accuracy formula on table
dectree.hypertunedaccuracy <- model.accuracy(cmdecisiontree.tuned)
#apply recall formula on yes cases of attrtiion
dectree.hypertunedrecallYes <- AttritionYes.recall(cmdecisiontree.tuned)
#apply precision formula on yes cases of attrtiion
dectree.hypertunedprecisionYes <- AttritionYes.precision(cmdecisiontree.tuned)
#calculate F1 Score for yes cases of attrtiion
dectree.hypertuned_F1ScoreYes <- round((2 * dectree.hypertunedprecisionYes * dectree.hypertunedrecallYes) / (dectree.hypertunedrecallYes
+dectree.hypertunedprecisionYes) ,digits=2)
#apply recall formula on no cases of attrtiion
dectree.hypertunedrecallNo <- AttritionNo.recall(cmdecisiontree.tuned)
#apply precision formula on no cases of attrtiion
dectree.hypertunedprecisionNo <- AttritionNo.precision(cmdecisiontree.tuned)
#calculate F1 Score for no cases of attrtiion
dectree.hypertuned_F1ScoreNo <- round((2 * dectree.hypertunedprecisionNo * dectree.hypertunedrecallNo) / (dectree.hypertunedrecallNo
+dectree.hypertunedprecisionNo) ,digits=2)
total.recall.hypertunedtree <- total.recall(cmdecisiontree.tuned) #apply total recall formula (both cases of attrtiion)
total.precision.hypertunedtree <- total.precision(cmdecisiontree.tuned) #apply total precision formula (both cases of attrtiion)
total.F1Score.hypertunedtree<- round((dectree.hypertuned_F1ScoreNo + dectree.hypertuned_F1ScoreYes)/2,digits=2) #calulate combined F1 score (both cases of attrtiion)
smote.model<- makeClassifTask(data = train_smote, target = "Attrition" ) # Convert training data to a classifcation task and store as new variable
smote.tree <- makeLearner("classif.rpart") # Sets learner for our classifcation task to rpart(decision tree)
smote.tunedTreePars <- tuneParams(smote.tree, task = smote.model, #classification task using decision tree
resampling = cvForTuning, # resampling strategy to be used based on previously defined input
par.set = treeParamSpace, # Parameter set to be used based on previously defined input
control = randSearch) #control strategy to be used based on previously defined input
parallelStop() # stop parallel run
[Tune] Started tuning learner classif.rpart for parameter set:
Type len Def Constr Req Tunable Trafo
minsplit integer - - 0 to 10 - TRUE -
minbucket integer - - 0 to 10 - TRUE -
cp numeric - - 0.01 to 0.1 - TRUE -
maxdepth integer - - 3 to 5 - TRUE -
With control class: TuneControlRandom
Imputation value: 1
[Tune-x] 1: minsplit=3; minbucket=5; cp=0.0874; maxdepth=4
[Tune-y] 1: mmce.test.mean=0.3030708; time: 0.0 min
[Tune-x] 2: minsplit=4; minbucket=2; cp=0.0164; maxdepth=4
[Tune-y] 2: mmce.test.mean=0.2713929; time: 0.0 min
[Tune-x] 3: minsplit=9; minbucket=5; cp=0.0258; maxdepth=4
[Tune-y] 3: mmce.test.mean=0.2666030; time: 0.0 min
[Tune-x] 4: minsplit=1; minbucket=4; cp=0.0368; maxdepth=5
[Tune-y] 4: mmce.test.mean=0.2812371; time: 0.0 min
[Tune-x] Setting hyperpars failed: Error in setHyperPars2.Learner(learner, insert(par.vals, args)) :
0 is not feasible for parameter 'minbucket'!
[Tune-x] 5: minsplit=5; minbucket=0; cp=0.0422; maxdepth=3
[Tune-y] 5: mmce.test.mean= NA; time: 0.0 min
[Tune-x] 6: minsplit=3; minbucket=5; cp=0.0503; maxdepth=5
[Tune-y] 6: mmce.test.mean=0.3018513; time: 0.0 min
[Tune-x] Setting hyperpars failed: Error in setHyperPars2.Learner(learner, insert(par.vals, args)) :
0 is not feasible for parameter 'minbucket'!
[Tune-x] 7: minsplit=4; minbucket=0; cp=0.0523; maxdepth=4
[Tune-y] 7: mmce.test.mean= NA; time: 0.0 min
[Tune-x] 8: minsplit=7; minbucket=8; cp=0.0685; maxdepth=4
[Tune-y] 8: mmce.test.mean=0.3030708; time: 0.0 min
[Tune-x] 9: minsplit=9; minbucket=3; cp=0.0122; maxdepth=3
[Tune-y] 9: mmce.test.mean=0.2896121; time: 0.0 min
[Tune-x] 10: minsplit=9; minbucket=9; cp=0.0571; maxdepth=4
[Tune-y] 10: mmce.test.mean=0.3030708; time: 0.0 min
[Tune-x] Setting hyperpars failed: Error in setHyperPars2.Learner(learner, insert(par.vals, args)) :
0 is not feasible for parameter 'minsplit'!
[Tune-x] 11: minsplit=0; minbucket=5; cp=0.0721; maxdepth=5
[Tune-y] 11: mmce.test.mean= NA; time: 0.0 min
[Tune-x] 12: minsplit=10; minbucket=8; cp=0.0509; maxdepth=5
[Tune-y] 12: mmce.test.mean=0.3018513; time: 0.0 min
[Tune-x] 13: minsplit=3; minbucket=9; cp=0.0929; maxdepth=4
[Tune-y] 13: mmce.test.mean=0.3030708; time: 0.0 min
[Tune-x] 14: minsplit=1; minbucket=3; cp=0.0133; maxdepth=5
[Tune-y] 14: mmce.test.mean=0.2617396; time: 0.0 min
[Tune-x] 15: minsplit=8; minbucket=4; cp=0.0286; maxdepth=3
[Tune-y] 15: mmce.test.mean=0.2957684; time: 0.0 min
[Tune-x] 16: minsplit=1; minbucket=7; cp=0.0913; maxdepth=5
[Tune-y] 16: mmce.test.mean=0.3030708; time: 0.0 min
[Tune-x] 17: minsplit=3; minbucket=5; cp=0.057; maxdepth=5
[Tune-y] 17: mmce.test.mean=0.3030708; time: 0.0 min
[Tune-x] 18: minsplit=7; minbucket=4; cp=0.0526; maxdepth=5
[Tune-y] 18: mmce.test.mean=0.3018513; time: 0.0 min
[Tune-x] 19: minsplit=10; minbucket=9; cp=0.0762; maxdepth=3
[Tune-y] 19: mmce.test.mean=0.3030708; time: 0.0 min
[Tune-x] 20: minsplit=2; minbucket=6; cp=0.043; maxdepth=5
[Tune-y] 20: mmce.test.mean=0.2908757; time: 0.0 min
[Tune] Result: minsplit=1; minbucket=3; cp=0.0133; maxdepth=5 : mmce.test.mean=0.2617396
smote.tunedTree <- setHyperPars(smote.tree, par.vals = smote.tunedTreePars$x) # set hyper parameters to best parameters from hypertuning/cross validation
smote.tunedTreeModel <- train(smote.tunedTree, smote.model) # train model using those parameters
smote.treeModelData <- getLearnerModel(smote.tunedTreeModel) # Get underlying R model of learner integrated into mlr
options(repr.plot.width=10, repr.plot.height=15) #customize width and height of plot
rpart.plot(smote.treeModelData, roundint = FALSE, #plot tree using rpart.plot library
box.palette = "BuBn",
type = 5)
#predict model using test set
smote.hypertunetree.preds<- predict(smote.treeModelData, test, type="class")
#store table of predicted and actual values
smote.cmdecisiontree.tuned <- table(test$Attrition, smote.hypertunetree.preds)
#apply accuracy formula on table
smote.dectree.hypertunedaccuracy <- model.accuracy(smote.cmdecisiontree.tuned)
#apply recall formula on yes cases of attrtiion
smote.dectree.hypertunedrecallYes <- AttritionYes.recall(smote.cmdecisiontree.tuned)
#apply precision formula on yes cases of attrtiion
smote.dectree.hypertunedprecisionYes <- AttritionYes.precision(smote.cmdecisiontree.tuned)
#calculate F1 Score for yes cases of attrtiion
smote.dectree.hypertuned_F1ScoreYes <- round((2 * smote.dectree.hypertunedprecisionYes * smote.dectree.hypertunedrecallYes) / (smote.dectree.hypertunedrecallYes
+smote.dectree.hypertunedprecisionYes) ,digits=2)
smote.dectree.hypertunedrecallNo <- AttritionNo.recall(smote.cmdecisiontree.tuned) #apply recall formula on no cases of attrtiion
smote.dectree.hypertunedprecisionNo <- AttritionNo.precision(smote.cmdecisiontree.tuned) #apply precision formula on no cases of attrtiion
#calculate F1 Score for no cases of attrtiion
smote.dectree.hypertuned_F1ScoreNo <- round((2 * smote.dectree.hypertunedprecisionNo * smote.dectree.hypertunedrecallNo) / (smote.dectree.hypertunedrecallNo
+smote.dectree.hypertunedprecisionNo) ,digits=2)
total.recall.smote.hypertunedtree <- total.recall(smote.cmdecisiontree.tuned)#apply total recall formula (both cases of attrtiion)
total.precision.smote.hypertunedtree <- total.precision(smote.cmdecisiontree.tuned)#apply total recall formula (both cases of attrtiion)
total.F1Score.smote.hypertunedtree<- round((smote.dectree.hypertuned_F1ScoreNo +
smote.dectree.hypertuned_F1ScoreYes)/2,digits=2)#calculate total F1 score (both cases of attrtiion)
#customize height and width of plot
options(repr.plot.width=8, repr.plot.height=6)
conf_df <- data.frame(table(test$Attrition, smote.hypertunetree.preds)) #store prediction results in a table
#plotting of confusion matrix using ggplot2 library
ggplot(data = conf_df, mapping = aes(x = smote.hypertunetree.preds, y = Var1)) +
geom_tile(aes(fill = Freq), colour = "white") +
geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1) +
scale_fill_gradient(low = "#F3F781", high = "#58FA82") + theme(legend.position="none", strip.background = element_blank(), strip.text.x = element_blank(),
plot.title=element_text(hjust=0.5, color="white"), plot.subtitle=element_text(colour="white"), plot.background=element_rect(fill="#0D7680"),
axis.text.x=element_text(colour="white"), axis.text.y=element_text(colour="white"),
axis.title=element_text(colour="white"),
legend.background = element_rect(fill="#FFF9F5",
size=0.5, linetype="solid",
colour ="black")) +
labs(title="Confusion Matrix Base XGBOOST", y="Actual Attrrition", x="Predicted Attrition")
postprune.tree <- prune(basetree, cp = 0.03 ) # prune base tree with complexity parameter set to 0.03
options(repr.plot.width=10, repr.plot.height=15) ##customize height and width of plot
rpart.plot(postprune.tree, roundint = FALSE, #plot tree using rpart.plot library
box.palette = "BuBn",
type = 5)
#predict model using test set
postprune.preds <- predict(postprune.tree, test, type="class")
#store table of predicted and actual values
cm.postprunetree <- table(test$Attrition, postprune.preds)
#apply accuracy formula on table
postprunetree.baseaccuracy <- model.accuracy(cm.postprunetree )
#apply recall formula on yes cases of attrtiion
postprunetree.baserecallYes <- AttritionYes.recall(cm.postprunetree)
#apply precision formula on yes cases of attrtiion
postprunetree.baseprecisionYes <- AttritionYes.precision(cm.postprunetree)
#calculate F1 Score for yes cases of attrtiion
postprunetree.base_F1ScoreYes <- round((2 * postprunetree.baseprecisionYes * postprunetree.baserecallYes) / (postprunetree.baserecallYes
+postprunetree.baseprecisionYes) ,digits=2)
postprunetree.baserecallNo <- AttritionNo.recall(cm.postprunetree) #apply recall formula on no cases of attrtiion
postprunetree.baseprecisionNo <- AttritionNo.precision(cm.postprunetree) #apply precision formula on no cases of attrtiion
#calculate F1 Score for no cases of attrtiion
postprunetree.base_F1ScoreNo <- round((2 * postprunetree.baseprecisionNo * postprunetree.baserecallNo) / (postprunetree.baserecallNo
+postprunetree.baseprecisionNo) ,digits=2)
total.recall.postprunetree <- total.recall(cm.postprunetree) #apply total recall formula (both cases of attrtiion)
total.precision.postprunetree <- total.precision(cm.postprunetree)#apply total precision formula (both cases of attrtiion)
total.F1Score.postprunetree<- round((postprunetree.base_F1ScoreYes + postprunetree.base_F1ScoreNo)/2,digits=2) #calculate total F1 score (both cases of attrtiion)
smote.postprune.tree <- prune(smotebasetree, cp = 0.03 ) # prune base tree with complexity parameter set to 0.03
options(repr.plot.width=10, repr.plot.height=15) ##customize height and width of plot
rpart.plot(smote.postprune.tree, roundint = FALSE, #plot tree using rpart.plot library
box.palette = "BuBn",
type = 5)
#predict model using test set
smote.postprune.preds <- predict(smote.postprune.tree, test, type="class")
#store table of predicted and actual values
smote.cm.postprunetree <- table(test$Attrition, smote.postprune.preds)
#apply accuracy formula on table
smote.postprunetree.baseaccuracy <- model.accuracy(smote.cm.postprunetree )
#apply recall formula on yes cases of attrtiion
smote.postprunetree.baserecallYes <- AttritionYes.recall(smote.cm.postprunetree)
#apply precision formula on yes cases of attrtiion
smote.postprunetree.baseprecisionYes <- AttritionYes.precision(smote.cm.postprunetree)
#calculate F1 Score for yes cases of attrtiion
smote.postprunetree.base_F1ScoreYes <- round((2 * smote.postprunetree.baseprecisionYes * smote.postprunetree.baserecallYes) / (smote.postprunetree.baserecallYes
+smote.postprunetree.baseprecisionYes) ,digits=2)
smote.postprunetree.baserecallNo <- AttritionNo.recall(smote.cm.postprunetree) #apply recall formula on no cases of attrtiion
smote.postprunetree.baseprecisionNo <- AttritionNo.precision(smote.cm.postprunetree) #apply precision formula on no cases of attrtiion
#calculate F1 Score for no cases of attrtiion
smote.postprunetree.base_F1ScoreNo <- round((2 * smote.postprunetree.baseprecisionNo * smote.postprunetree.baserecallNo) / (smote.postprunetree.baserecallNo
+smote.postprunetree.baseprecisionNo) ,digits=2)
total.recall.smote.postprunetree <- total.recall(smote.cm.postprunetree) #apply total recall formula (both cases of attrtiion)
total.precision.smote.postprunetree <- total.precision(smote.cm.postprunetree) #apply total precision formula (both cases of attrtiion)
total.F1Score.smote.postprunetree<- round((smote.postprunetree.base_F1ScoreYes +
smote.postprunetree.base_F1ScoreNo)/2,digits=2) #calculate total F1 score (both cases of attrtiion)
base.train <- train #assign train data to a new variable base.train
base.test <- df[inds$test, ] #assign test data to a new variable base.train
setDT(base.train) # Set base.train to a data table
setDT(base.test) # Set base.test to a data table
base.train_label <- base.train$Attrition # assign target label for train set to a new field base.train_label
base.test_label <- base.test$Attrition # assign target label for test set to a new field base.train_label
base.new_train <- model.matrix(~.+0,data = base.train[,-c("Attrition"),with=F]) # convert base.train to matrix format and assign to new variable base.new_train
base.new_test <- model.matrix(~.+0,data = base.test[,-c("Attrition"),with=F]) # convert base.test to matrix format and assign to new variable base.new_test
base.train_label <- as.numeric(base.train_label)-1 #convert base.train_label to numeric - 1 so we have (0,1) instead of(1,2)
base.test_label <- as.numeric(base.test_label)-1 #convert base.test_label label to numeric - 1 so we have (0,1) instead of(1,2)
base.dtrain <- xgb.DMatrix(data = base.new_train,label = base.train_label ) #Construct xgb.DMatrix object and assign to base.dtrain
base.dtest <- xgb.DMatrix(data = base.new_test,label = base.test_label) #Construct xgb.DMatrix object and assign to base.dtest
#Train base xgb model with no hyperparameter tuning/cross validation
base.xgb <- xgb.train (data = base.dtrain, nrounds = 79, watchlist = list(val=base.dtest,train=base.dtrain), print_every_n = 10,
early_stopping_rounds = 10, maximize = F , eval_metric = "error")
[1] val-error:0.149321 train-error:0.087549 Multiple eval metrics are present. Will use train_error for early stopping. Will train until train_error hasn't improved in 10 rounds. [11] val-error:0.135747 train-error:0.025292 [21] val-error:0.135747 train-error:0.007782 [31] val-error:0.128959 train-error:0.001946 [41] val-error:0.133484 train-error:0.000000 Stopping. Best iteration: [35] val-error:0.128959 train-error:0.000000
base.xgbpred <- predict (base.xgb,base.dtest) #predict model using test set
base.xgbpred <- ifelse(base.xgbpred > 0.5,1,0)#if probablity > 0.5 set prediction to class 1 else set predictionto class 0
#Feature importance plot for base xgboost model
base.mat <- xgb.importance (feature_names = colnames(base.new_train),model = base.xgb)
xgb.plot.importance (importance_matrix = base.mat[1:10] , rel_to_first = TRUE, xlab = "Relative importance Base XGBOOST" )
base.test$Attrition <- as.numeric(base.test$Attrition)-1 #convert target label to numeric - 1 so we have (0,1) instead of(1,2)
#customize height and width of plot
options(repr.plot.width=8, repr.plot.height=6)
#store table of predicted and actual values as conf_df
conf_df <- data.frame(table(base.test$Attrition, base.xgbpred))
#plotting of confusion matrix using ggplot2 library
ggplot(data = conf_df, mapping = aes(x = base.xgbpred, y = Var1)) +
geom_tile(aes(fill = Freq), colour = "white") +
geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1) +
scale_fill_gradient(low = "#F3F781", high = "#58FA82") + theme(legend.position="none", strip.background = element_blank(), strip.text.x = element_blank(),
plot.title=element_text(hjust=0.5, color="white"), plot.subtitle=element_text(colour="white"), plot.background=element_rect(fill="#0D7680"),
axis.text.x=element_text(colour="white"), axis.text.y=element_text(colour="white"),
axis.title=element_text(colour="white"),
legend.background = element_rect(fill="#FFF9F5",
size=0.5, linetype="solid",
colour ="black")) +
labs(title="Confusion Matrix Base XGBOOST", y="Actual Attrrition", x="Predicted Attrition")
#xgb.plot.tree(model=base.xgb, tree=0)
#xgb.plot.tree(model=base.xgb, tree=0)
#store table of predicted and actual values
cm.basexgboost <- table(base.test$Attrition, base.xgbpred)
#apply accuracy formula on table
xgboost.baseaccuracy <- model.accuracy(cm.basexgboost)
#apply recall formula on yes cases of attrtiion
xgboost.baserecallYes <- AttritionYes.recall(cm.basexgboost)
#apply precision formula on yes cases of attrtiion
xgboost.baseprecisionYes <- AttritionYes.precision(cm.basexgboost)
#calculate F1 Score for yes cases of attrtiion
xgboost.base_F1ScoreYes <- round((2 * xgboost.baseprecisionYes * xgboost.baserecallYes) / (xgboost.baserecallYes
+xgboost.baseprecisionYes) ,digits=2)
xgboost.baserecallNo <- AttritionNo.recall(cm.basexgboost) #apply recall formula on no cases of attrtiion
xgboost.baseprecisionNo <- AttritionNo.precision(cm.basexgboost) #apply precision formula on no cases of attrtiion
#calculate F1 Score for no cases of attrtiion
xgboost.base_F1ScoreNo <- round((2 * xgboost.baseprecisionNo * xgboost.baserecallNo) / (xgboost.baserecallNo
+xgboost.baseprecisionNo) ,digits=2)
total.recall.base_XGBoost<- total.recall(cm.basexgboost) #apply total recall formula (both cases of attrtiion)
total.precision.base_XGBoost <- total.precision(cm.basexgboost) #apply total precision formula (both cases of attrtiion)
total.F1Score.base_XGBoost<- round((xgboost.base_F1ScoreYes + xgboost.base_F1ScoreNo)/2,digits=2) #calculate total F1 score (both cases of attrtiion)
smoteXgboost.train <- train_smote #assign train data to a new variable smoteXgboost.train
smoteXgboost.test <- df[inds$test, ] #assign test data to a new variable smoteXgboost.test
setDT(smoteXgboost.train) # Set smoteXgboost.train to a data table
setDT(smoteXgboost.test) # Set smoteXgboost.test to a data table
smote.base.train_label <- smoteXgboost.train$Attrition # assign target label for train set to a new field smote.base.train_label
smote.base.test_label <- smoteXgboost.test $Attrition # assign target label for test set to a new field smote.base.test_label
smote.base.new_train <- model.matrix(~.+0,data = smoteXgboost.train[,-c("Attrition"),with=F]) # convert base.train to matrix format and assign to new variable smote.base.new_train
smote.base.new_test <- model.matrix(~.+0,data = smoteXgboost.test[,-c("Attrition"),with=F]) # convert base.test to matrix format and assign to new variable smote.base.new_test
smote.base.train_label <- as.numeric(smote.base.train_label)-1 #convert smote.base.train_label to numeric - 1 so we have (0,1) instead of(1,2)
smote.base.test_label <- as.numeric(smote.base.test_label)-1 #convert smote.base.test_label to numeric - 1 so we have (0,1) instead of(1,2)
smote.base.dtrain <- xgb.DMatrix(data = smote.base.new_train,label = smote.base.train_label ) #Construct xgb.DMatrix object and assign to smote.base.dtrain
smote.base.dtest <- xgb.DMatrix(data = smote.base.new_test,label = smote.base.test_label) #Construct xgb.DMatrix object and assign to smote.base.dtest
#Train oversampled xgb model with no hyperparameter tuning/cross validation
smote.base.xgb <- xgb.train (data = smote.base.dtrain, nrounds = 79, watchlist = list(val=smote.base.dtest,train=smote.base.dtrain), print_every_n = 10,
early_stopping_rounds = 10, maximize = F , eval_metric = "error")
[1] val-error:0.418552 train-error:0.146667 Multiple eval metrics are present. Will use train_error for early stopping. Will train until train_error hasn't improved in 10 rounds. [11] val-error:0.332579 train-error:0.002424 [21] val-error:0.332579 train-error:0.000000 Stopping. Best iteration: [16] val-error:0.334842 train-error:0.000000
smote.base.xgbpred <- predict (smote.base.xgb,smote.base.dtest) #predict model using test set
smote.base.xgbpred <- ifelse(smote.base.xgbpred > 0.5,1,0) #if probablity > 0.5 set prediction to class 1 else set predictionto class 0
#Feature importance plot for base xgboost model
smote.base.mat <- xgb.importance (feature_names = colnames(smote.base.new_train),model = smote.base.xgb)
xgb.plot.importance (importance_matrix = smote.base.mat[1:10] , rel_to_first = TRUE, xlab = "Relative importance Smote Base XGBOOST" )
smoteXgboost.test$Attrition <- as.numeric(smoteXgboost.test$Attrition)-1 #convert target label to numeric - 1 so we have (0,1) instead of(1,2)
#customize height and width of plot
options(repr.plot.width=8, repr.plot.height=6)
conf_df <- data.frame(table(smoteXgboost.test$Attrition, smote.base.xgbpred))
#plotting of confusion matrix using ggplot2 library
ggplot(data = conf_df, mapping = aes(x = smote.base.xgbpred, y = Var1)) +
geom_tile(aes(fill = Freq), colour = "white") +
geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1) +
scale_fill_gradient(low = "#F3F781", high = "#58FA82") + theme(legend.position="none", strip.background = element_blank(), strip.text.x = element_blank(),
plot.title=element_text(hjust=0.5, color="white"), plot.subtitle=element_text(colour="white"), plot.background=element_rect(fill="#0D7680"),
axis.text.x=element_text(colour="white"), axis.text.y=element_text(colour="white"),
axis.title=element_text(colour="white"),
legend.background = element_rect(fill="#FFF9F5",
size=0.5, linetype="solid",
colour ="black")) +
labs(title="Confusion Matrix Base XGBOOST", y="Actual Attrrition", x="Predicted Attrition")
#xgb.plot.tree(model=smote.base.xgb, tree=0)
#xgb.plot.tree(model=smote.base.xgb, tree=1)
#store table of predicted and actual values
cm.smote.basexgboost <- table(smoteXgboost.test$Attrition, smote.base.xgbpred)
#apply accuracy formula on table
smote.xgboost.baseaccuracy <- model.accuracy(cm.smote.basexgboost)
#apply recall formula on yes cases of attrtiion
smote.xgboost.baserecallYes <- AttritionYes.recall(cm.smote.basexgboost)
#apply precision formula on yes cases of attrtiion
smote.xgboost.baseprecisionYes <- AttritionYes.precision(cm.smote.basexgboost)
#calculate F1 Score for yes cases of attrtiion
smote.xgboost.base_F1ScoreYes <- round((2 * smote.xgboost.baseprecisionYes * smote.xgboost.baserecallYes) / (smote.xgboost.baserecallYes
+smote.xgboost.baseprecisionYes) ,digits=2)
smote.xgboost.baserecallNo <- AttritionNo.recall(cm.smote.basexgboost)#apply recall formula on no cases of attrtiion
smote.xgboost.baseprecisionNo <- AttritionNo.precision(cm.smote.basexgboost) #apply precision formula on no cases of attrtiion
#calculate F1 Score for no cases of attrtiion
smote.xgboost.base_F1ScoreNo <- round((2 * smote.xgboost.baseprecisionNo * smote.xgboost.baserecallNo) / (smote.xgboost.baserecallNo
+smote.xgboost.baseprecisionNo) ,digits=2)
total.recall.smote.base_XGBoost<- total.recall(cm.smote.basexgboost) #apply total recall formula (both cases of attrtiion)
total.precision.smote.base_XGBoost <- total.precision(cm.smote.basexgboost) #apply total precision formula (both cases of attrtiion)
total.F1Score.smote.base_XGBoost<- round((smote.xgboost.base_F1ScoreYes + smote.xgboost.base_F1ScoreNo)/2,digits=2) #calculate total F1 score (both cases of attrtiion)
#train test splits using partition function (70 percent train , 30 percent test)
tuned.inds <- partition(df$Attrition, p = c(train = 0.7,test = 0.3))
tuned.train <- df[tuned.inds$train, ]
tuned.test <- df[tuned.inds$test, ]
fact_col <- colnames(tuned.train)[sapply(tuned.train,is.character)] # check for charactar columns and store them in fact_col variable
for(i in fact_col) set(tuned.train,j=i,value = factor(tuned.train[[i]])) # convert training data to factor
for (i in fact_col) set(tuned.test,j=i,value = factor(tuned.test[[i]])) # convert test data to factor
tuned.traintask <- makeClassifTask (data = tuned.train,target = "Attrition") #create classification task for train set
tuned.testtask <- makeClassifTask (data = tuned.test,target = "Attrition") #create classification task for test set
#one hot encoding train and test classification tasks
tuned.traintask <- createDummyFeatures (obj = tuned.traintask )
tuned.testtask <- createDummyFeatures (obj = tuned.testtask )
#create learner to be used for model training
lrn <- makeLearner("classif.xgboost",predict.type = "response")
#Specify parameter values for the learner previously defined
lrn$par.vals <- list( objective="binary:logistic", eval_metric="error", nrounds=100L, eta=0.1)
#Set parameters to be used for hyperparameter tuning
params <- makeParamSet(makeIntegerParam("max_depth",lower = 3L,upper = 10L), makeNumericParam("min_child_weight",lower = 1L,upper = 10L), makeNumericParam("subsample",lower = 0.5,upper = 1), makeNumericParam("colsample_bytree",lower = 0.5,upper = 1))
#set resampling strategy to 5 fold cross validation . Set stratify = True
rdesc <- makeResampleDesc("CV",stratify = T,iters=5L)
ctrl <- makeTuneControlRandom(maxit = 10L) #Set number of iterations for random search to a maximum of 10
#train xgboost boost model
mytune <- tuneParams(learner = lrn, task = tuned.traintask, resampling = rdesc, measures = acc, par.set = params, control = ctrl, show.info = T)
[Tune] Started tuning learner classif.xgboost for parameter set:
Type len Def Constr Req Tunable Trafo
max_depth integer - - 3 to 10 - TRUE -
min_child_weight numeric - - 1 to 10 - TRUE -
subsample numeric - - 0.5 to 1 - TRUE -
colsample_bytree numeric - - 0.5 to 1 - TRUE -
With control class: TuneControlRandom
Imputation value: -0
[Tune-x] 1: max_depth=6; min_child_weight=4.51; subsample=0.522; colsample_bytree=0.669
[1] train-error:0.156934 [2] train-error:0.142336 [3] train-error:0.139903 [4] train-error:0.139903 [5] train-error:0.138686 [6] train-error:0.137470 [7] train-error:0.139903 [8] train-error:0.141119 [9] train-error:0.141119 [10] train-error:0.139903 [11] train-error:0.142336 [12] train-error:0.138686 [13] train-error:0.141119 [14] train-error:0.142336 [15] train-error:0.138686 [16] train-error:0.139903 [17] train-error:0.138686 [18] train-error:0.136253 [19] train-error:0.133820 [20] train-error:0.135036 [21] train-error:0.133820 [22] train-error:0.131387 [23] train-error:0.130170 [24] train-error:0.128954 [25] train-error:0.128954 [26] train-error:0.125304 [27] train-error:0.121655 [28] train-error:0.125304 [29] train-error:0.119221 [30] train-error:0.120438 [31] train-error:0.115572 [32] train-error:0.114355 [33] train-error:0.108273 [34] train-error:0.109489 [35] train-error:0.109489 [36] train-error:0.107056 [37] train-error:0.105839 [38] train-error:0.107056 [39] train-error:0.109489 [40] train-error:0.105839 [41] train-error:0.105839 [42] train-error:0.102190 [43] train-error:0.100973 [44] train-error:0.096107 [45] train-error:0.096107 [46] train-error:0.096107 [47] train-error:0.096107 [48] train-error:0.093674 [49] train-error:0.093674 [50] train-error:0.094891 [51] train-error:0.090024 [52] train-error:0.086375 [53] train-error:0.090024 [54] train-error:0.088808 [55] train-error:0.087591 [56] train-error:0.092457 [57] train-error:0.090024 [58] train-error:0.091241 [59] train-error:0.090024 [60] train-error:0.086375 [61] train-error:0.085158 [62] train-error:0.086375 [63] train-error:0.086375 [64] train-error:0.086375 [65] train-error:0.085158 [66] train-error:0.085158 [67] train-error:0.083942 [68] train-error:0.082725 [69] train-error:0.082725 [70] train-error:0.080292 [71] train-error:0.080292 [72] train-error:0.080292 [73] train-error:0.080292 [74] train-error:0.077859 [75] train-error:0.072993 [76] train-error:0.071776 [77] train-error:0.074209 [78] train-error:0.069343 [79] train-error:0.074209 [80] train-error:0.070560 [81] train-error:0.070560 [82] train-error:0.070560 [83] train-error:0.068127 [84] train-error:0.065693 [85] train-error:0.065693 [86] train-error:0.068127 [87] train-error:0.064477 [88] train-error:0.065693 [89] train-error:0.066910 [90] train-error:0.062044 [91] train-error:0.062044 [92] train-error:0.060827 [93] train-error:0.059611 [94] train-error:0.058394 [95] train-error:0.059611 [96] train-error:0.058394 [97] train-error:0.057178 [98] train-error:0.057178 [99] train-error:0.059611 [100] train-error:0.059611 [1] train-error:0.136253 [2] train-error:0.130170 [3] train-error:0.121655 [4] train-error:0.122871 [5] train-error:0.130170 [6] train-error:0.125304 [7] train-error:0.131387 [8] train-error:0.135036 [9] train-error:0.131387 [10] train-error:0.126521 [11] train-error:0.126521 [12] train-error:0.127737 [13] train-error:0.125304 [14] train-error:0.126521 [15] train-error:0.126521 [16] train-error:0.126521 [17] train-error:0.124088 [18] train-error:0.121655 [19] train-error:0.121655 [20] train-error:0.118005 [21] train-error:0.114355 [22] train-error:0.113139 [23] train-error:0.110706 [24] train-error:0.114355 [25] train-error:0.110706 [26] train-error:0.109489 [27] train-error:0.111922 [28] train-error:0.110706 [29] train-error:0.113139 [30] train-error:0.111922 [31] train-error:0.108273 [32] train-error:0.108273 [33] train-error:0.110706 [34] train-error:0.113139 [35] train-error:0.108273 [36] train-error:0.102190 [37] train-error:0.103406 [38] train-error:0.099757 [39] train-error:0.099757 [40] train-error:0.098540 [41] train-error:0.096107 [42] train-error:0.094891 [43] train-error:0.096107 [44] train-error:0.094891 [45] train-error:0.092457 [46] train-error:0.091241 [47] train-error:0.090024 [48] train-error:0.090024 [49] train-error:0.090024 [50] train-error:0.090024 [51] train-error:0.088808 [52] train-error:0.090024 [53] train-error:0.090024 [54] train-error:0.088808 [55] train-error:0.087591 [56] train-error:0.086375 [57] train-error:0.086375 [58] train-error:0.085158 [59] train-error:0.085158 [60] train-error:0.083942 [61] train-error:0.081509 [62] train-error:0.082725 [63] train-error:0.085158 [64] train-error:0.082725 [65] train-error:0.080292 [66] train-error:0.079075 [67] train-error:0.080292 [68] train-error:0.077859 [69] train-error:0.082725 [70] train-error:0.080292 [71] train-error:0.080292 [72] train-error:0.077859 [73] train-error:0.076642 [74] train-error:0.076642 [75] train-error:0.075426 [76] train-error:0.072993 [77] train-error:0.071776 [78] train-error:0.072993 [79] train-error:0.072993 [80] train-error:0.074209 [81] train-error:0.072993 [82] train-error:0.071776 [83] train-error:0.066910 [84] train-error:0.068127 [85] train-error:0.068127 [86] train-error:0.066910 [87] train-error:0.066910 [88] train-error:0.070560 [89] train-error:0.068127 [90] train-error:0.066910 [91] train-error:0.070560 [92] train-error:0.070560 [93] train-error:0.069343 [94] train-error:0.068127 [95] train-error:0.068127 [96] train-error:0.068127 [97] train-error:0.066910 [98] train-error:0.066910 [99] train-error:0.066910 [100] train-error:0.066910 [1] train-error:0.151883 [2] train-error:0.133657 [3] train-error:0.142163 [4] train-error:0.143378 [5] train-error:0.138518 [6] train-error:0.136087 [7] train-error:0.138518 [8] train-error:0.128797 [9] train-error:0.125152 [10] train-error:0.123937 [11] train-error:0.130012 [12] train-error:0.131227 [13] train-error:0.127582 [14] train-error:0.126367 [15] train-error:0.122722 [16] train-error:0.121507 [17] train-error:0.122722 [18] train-error:0.125152 [19] train-error:0.126367 [20] train-error:0.120292 [21] train-error:0.121507 [22] train-error:0.121507 [23] train-error:0.122722 [24] train-error:0.121507 [25] train-error:0.120292 [26] train-error:0.120292 [27] train-error:0.120292 [28] train-error:0.121507 [29] train-error:0.121507 [30] train-error:0.119077 [31] train-error:0.117861 [32] train-error:0.114216 [33] train-error:0.115431 [34] train-error:0.114216 [35] train-error:0.113001 [36] train-error:0.111786 [37] train-error:0.111786 [38] train-error:0.110571 [39] train-error:0.104496 [40] train-error:0.103281 [41] train-error:0.105711 [42] train-error:0.103281 [43] train-error:0.102066 [44] train-error:0.099635 [45] train-error:0.098420 [46] train-error:0.097205 [47] train-error:0.095990 [48] train-error:0.095990 [49] train-error:0.093560 [50] train-error:0.093560 [51] train-error:0.091130 [52] train-error:0.094775 [53] train-error:0.092345 [54] train-error:0.093560 [55] train-error:0.092345 [56] train-error:0.092345 [57] train-error:0.091130 [58] train-error:0.088700 [59] train-error:0.089915 [60] train-error:0.088700 [61] train-error:0.091130 [62] train-error:0.086270 [63] train-error:0.086270 [64] train-error:0.085055 [65] train-error:0.086270 [66] train-error:0.087485 [67] train-error:0.088700 [68] train-error:0.086270 [69] train-error:0.086270 [70] train-error:0.081409 [71] train-error:0.081409 [72] train-error:0.082625 [73] train-error:0.083840 [74] train-error:0.083840 [75] train-error:0.081409 [76] train-error:0.080194 [77] train-error:0.077764 [78] train-error:0.075334 [79] train-error:0.074119 [80] train-error:0.072904 [81] train-error:0.074119 [82] train-error:0.072904 [83] train-error:0.072904 [84] train-error:0.076549 [85] train-error:0.070474 [86] train-error:0.071689 [87] train-error:0.066829 [88] train-error:0.069259 [89] train-error:0.068044 [90] train-error:0.070474 [91] train-error:0.074119 [92] train-error:0.068044 [93] train-error:0.069259 [94] train-error:0.069259 [95] train-error:0.066829 [96] train-error:0.066829 [97] train-error:0.066829 [98] train-error:0.064399 [99] train-error:0.063183 [100] train-error:0.064399 [1] train-error:0.147023 [2] train-error:0.133657 [3] train-error:0.125152 [4] train-error:0.131227 [5] train-error:0.132442 [6] train-error:0.132442 [7] train-error:0.128797 [8] train-error:0.131227 [9] train-error:0.132442 [10] train-error:0.130012 [11] train-error:0.130012 [12] train-error:0.123937 [13] train-error:0.123937 [14] train-error:0.126367 [15] train-error:0.123937 [16] train-error:0.122722 [17] train-error:0.123937 [18] train-error:0.126367 [19] train-error:0.121507 [20] train-error:0.121507 [21] train-error:0.121507 [22] train-error:0.119077 [23] train-error:0.116646 [24] train-error:0.117861 [25] train-error:0.113001 [26] train-error:0.110571 [27] train-error:0.110571 [28] train-error:0.108141 [29] train-error:0.103281 [30] train-error:0.104496 [31] train-error:0.102066 [32] train-error:0.104496 [33] train-error:0.103281 [34] train-error:0.102066 [35] train-error:0.098420 [36] train-error:0.099635 [37] train-error:0.100851 [38] train-error:0.100851 [39] train-error:0.099635 [40] train-error:0.099635 [41] train-error:0.095990 [42] train-error:0.094775 [43] train-error:0.093560 [44] train-error:0.094775 [45] train-error:0.087485 [46] train-error:0.091130 [47] train-error:0.093560 [48] train-error:0.091130 [49] train-error:0.088700 [50] train-error:0.089915 [51] train-error:0.088700 [52] train-error:0.091130 [53] train-error:0.091130 [54] train-error:0.087485 [55] train-error:0.089915 [56] train-error:0.088700 [57] train-error:0.089915 [58] train-error:0.085055 [59] train-error:0.082625 [60] train-error:0.082625 [61] train-error:0.081409 [62] train-error:0.081409 [63] train-error:0.082625 [64] train-error:0.081409 [65] train-error:0.081409 [66] train-error:0.083840 [67] train-error:0.083840 [68] train-error:0.085055 [69] train-error:0.085055 [70] train-error:0.081409 [71] train-error:0.078979 [72] train-error:0.078979 [73] train-error:0.077764 [74] train-error:0.077764 [75] train-error:0.077764 [76] train-error:0.077764 [77] train-error:0.075334 [78] train-error:0.072904 [79] train-error:0.072904 [80] train-error:0.072904 [81] train-error:0.074119 [82] train-error:0.072904 [83] train-error:0.072904 [84] train-error:0.074119 [85] train-error:0.070474 [86] train-error:0.071689 [87] train-error:0.070474 [88] train-error:0.068044 [89] train-error:0.069259 [90] train-error:0.069259 [91] train-error:0.070474 [92] train-error:0.070474 [93] train-error:0.069259 [94] train-error:0.069259 [95] train-error:0.066829 [96] train-error:0.068044 [97] train-error:0.064399 [98] train-error:0.065614 [99] train-error:0.066829 [100] train-error:0.064399 [1] train-error:0.149635 [2] train-error:0.141119 [3] train-error:0.144769 [4] train-error:0.136253 [5] train-error:0.136253 [6] train-error:0.135036 [7] train-error:0.136253 [8] train-error:0.128954 [9] train-error:0.131387 [10] train-error:0.121655 [11] train-error:0.124088 [12] train-error:0.124088 [13] train-error:0.124088 [14] train-error:0.126521 [15] train-error:0.126521 [16] train-error:0.122871 [17] train-error:0.121655 [18] train-error:0.119221 [19] train-error:0.120438 [20] train-error:0.121655 [21] train-error:0.121655 [22] train-error:0.119221 [23] train-error:0.115572 [24] train-error:0.115572 [25] train-error:0.119221 [26] train-error:0.118005 [27] train-error:0.116788 [28] train-error:0.115572 [29] train-error:0.113139 [30] train-error:0.110706 [31] train-error:0.104623 [32] train-error:0.100973 [33] train-error:0.104623 [34] train-error:0.103406 [35] train-error:0.103406 [36] train-error:0.105839 [37] train-error:0.104623 [38] train-error:0.102190 [39] train-error:0.102190 [40] train-error:0.097324 [41] train-error:0.096107 [42] train-error:0.096107 [43] train-error:0.096107 [44] train-error:0.097324 [45] train-error:0.092457 [46] train-error:0.093674 [47] train-error:0.087591 [48] train-error:0.088808 [49] train-error:0.088808 [50] train-error:0.086375 [51] train-error:0.085158 [52] train-error:0.083942 [53] train-error:0.083942 [54] train-error:0.081509 [55] train-error:0.080292 [56] train-error:0.079075 [57] train-error:0.081509 [58] train-error:0.081509 [59] train-error:0.079075 [60] train-error:0.081509 [61] train-error:0.077859 [62] train-error:0.076642 [63] train-error:0.077859 [64] train-error:0.077859 [65] train-error:0.076642 [66] train-error:0.077859 [67] train-error:0.077859 [68] train-error:0.076642 [69] train-error:0.076642 [70] train-error:0.074209 [71] train-error:0.072993 [72] train-error:0.071776 [73] train-error:0.071776 [74] train-error:0.072993 [75] train-error:0.072993 [76] train-error:0.075426 [77] train-error:0.071776 [78] train-error:0.070560 [79] train-error:0.069343 [80] train-error:0.069343 [81] train-error:0.068127 [82] train-error:0.065693 [83] train-error:0.066910 [84] train-error:0.065693 [85] train-error:0.065693 [86] train-error:0.068127 [87] train-error:0.065693 [88] train-error:0.065693 [89] train-error:0.064477 [90] train-error:0.068127 [91] train-error:0.068127 [92] train-error:0.068127 [93] train-error:0.066910 [94] train-error:0.066910 [95] train-error:0.068127 [96] train-error:0.064477 [97] train-error:0.064477 [98] train-error:0.065693 [99] train-error:0.065693 [100] train-error:0.063260
[Tune-y] 1: acc.test.mean=0.8754914; time: 0.0 min [Tune-x] 2: max_depth=8; min_child_weight=4.71; subsample=0.85; colsample_bytree=0.723
[1] train-error:0.137470 [2] train-error:0.136253 [3] train-error:0.131387 [4] train-error:0.127737 [5] train-error:0.119221 [6] train-error:0.120438 [7] train-error:0.116788 [8] train-error:0.113139 [9] train-error:0.113139 [10] train-error:0.109489 [11] train-error:0.110706 [12] train-error:0.110706 [13] train-error:0.108273 [14] train-error:0.108273 [15] train-error:0.107056 [16] train-error:0.108273 [17] train-error:0.107056 [18] train-error:0.105839 [19] train-error:0.105839 [20] train-error:0.102190 [21] train-error:0.100973 [22] train-error:0.100973 [23] train-error:0.096107 [24] train-error:0.093674 [25] train-error:0.096107 [26] train-error:0.096107 [27] train-error:0.093674 [28] train-error:0.094891 [29] train-error:0.090024 [30] train-error:0.090024 [31] train-error:0.088808 [32] train-error:0.086375 [33] train-error:0.086375 [34] train-error:0.082725 [35] train-error:0.079075 [36] train-error:0.081509 [37] train-error:0.079075 [38] train-error:0.080292 [39] train-error:0.081509 [40] train-error:0.079075 [41] train-error:0.077859 [42] train-error:0.080292 [43] train-error:0.079075 [44] train-error:0.075426 [45] train-error:0.074209 [46] train-error:0.070560 [47] train-error:0.072993 [48] train-error:0.072993 [49] train-error:0.069343 [50] train-error:0.068127 [51] train-error:0.066910 [52] train-error:0.066910 [53] train-error:0.065693 [54] train-error:0.063260 [55] train-error:0.063260 [56] train-error:0.060827 [57] train-error:0.059611 [58] train-error:0.059611 [59] train-error:0.058394 [60] train-error:0.057178 [61] train-error:0.055961 [62] train-error:0.053528 [63] train-error:0.053528 [64] train-error:0.054745 [65] train-error:0.051095 [66] train-error:0.051095 [67] train-error:0.049878 [68] train-error:0.047445 [69] train-error:0.046229 [70] train-error:0.042579 [71] train-error:0.043796 [72] train-error:0.043796 [73] train-error:0.041363 [74] train-error:0.042579 [75] train-error:0.042579 [76] train-error:0.041363 [77] train-error:0.038929 [78] train-error:0.037713 [79] train-error:0.038929 [80] train-error:0.038929 [81] train-error:0.036496 [82] train-error:0.034063 [83] train-error:0.036496 [84] train-error:0.031630 [85] train-error:0.031630 [86] train-error:0.030414 [87] train-error:0.030414 [88] train-error:0.029197 [89] train-error:0.027981 [90] train-error:0.027981 [91] train-error:0.031630 [92] train-error:0.034063 [93] train-error:0.032847 [94] train-error:0.030414 [95] train-error:0.030414 [96] train-error:0.029197 [97] train-error:0.029197 [98] train-error:0.029197 [99] train-error:0.031630 [100] train-error:0.030414 [1] train-error:0.145985 [2] train-error:0.118005 [3] train-error:0.120438 [4] train-error:0.119221 [5] train-error:0.116788 [6] train-error:0.116788 [7] train-error:0.114355 [8] train-error:0.110706 [9] train-error:0.110706 [10] train-error:0.109489 [11] train-error:0.107056 [12] train-error:0.110706 [13] train-error:0.111922 [14] train-error:0.108273 [15] train-error:0.109489 [16] train-error:0.108273 [17] train-error:0.104623 [18] train-error:0.102190 [19] train-error:0.100973 [20] train-error:0.098540 [21] train-error:0.098540 [22] train-error:0.098540 [23] train-error:0.098540 [24] train-error:0.094891 [25] train-error:0.094891 [26] train-error:0.094891 [27] train-error:0.092457 [28] train-error:0.093674 [29] train-error:0.087591 [30] train-error:0.088808 [31] train-error:0.087591 [32] train-error:0.087591 [33] train-error:0.086375 [34] train-error:0.086375 [35] train-error:0.085158 [36] train-error:0.083942 [37] train-error:0.081509 [38] train-error:0.081509 [39] train-error:0.079075 [40] train-error:0.079075 [41] train-error:0.076642 [42] train-error:0.074209 [43] train-error:0.070560 [44] train-error:0.071776 [45] train-error:0.068127 [46] train-error:0.066910 [47] train-error:0.068127 [48] train-error:0.065693 [49] train-error:0.065693 [50] train-error:0.064477 [51] train-error:0.065693 [52] train-error:0.063260 [53] train-error:0.063260 [54] train-error:0.065693 [55] train-error:0.064477 [56] train-error:0.063260 [57] train-error:0.063260 [58] train-error:0.063260 [59] train-error:0.063260 [60] train-error:0.062044 [61] train-error:0.062044 [62] train-error:0.059611 [63] train-error:0.059611 [64] train-error:0.057178 [65] train-error:0.055961 [66] train-error:0.058394 [67] train-error:0.055961 [68] train-error:0.054745 [69] train-error:0.054745 [70] train-error:0.057178 [71] train-error:0.055961 [72] train-error:0.053528 [73] train-error:0.049878 [74] train-error:0.051095 [75] train-error:0.051095 [76] train-error:0.049878 [77] train-error:0.045012 [78] train-error:0.045012 [79] train-error:0.045012 [80] train-error:0.046229 [81] train-error:0.043796 [82] train-error:0.045012 [83] train-error:0.043796 [84] train-error:0.042579 [85] train-error:0.041363 [86] train-error:0.040146 [87] train-error:0.040146 [88] train-error:0.040146 [89] train-error:0.040146 [90] train-error:0.038929 [91] train-error:0.037713 [92] train-error:0.038929 [93] train-error:0.036496 [94] train-error:0.035280 [95] train-error:0.034063 [96] train-error:0.036496 [97] train-error:0.036496 [98] train-error:0.034063 [99] train-error:0.034063 [100] train-error:0.031630 [1] train-error:0.139733 [2] train-error:0.121507 [3] train-error:0.121507 [4] train-error:0.117861 [5] train-error:0.117861 [6] train-error:0.121507 [7] train-error:0.120292 [8] train-error:0.120292 [9] train-error:0.119077 [10] train-error:0.119077 [11] train-error:0.117861 [12] train-error:0.113001 [13] train-error:0.114216 [14] train-error:0.111786 [15] train-error:0.110571 [16] train-error:0.110571 [17] train-error:0.106926 [18] train-error:0.106926 [19] train-error:0.103281 [20] train-error:0.104496 [21] train-error:0.103281 [22] train-error:0.100851 [23] train-error:0.100851 [24] train-error:0.098420 [25] train-error:0.098420 [26] train-error:0.095990 [27] train-error:0.095990 [28] train-error:0.095990 [29] train-error:0.092345 [30] train-error:0.092345 [31] train-error:0.087485 [32] train-error:0.089915 [33] train-error:0.089915 [34] train-error:0.087485 [35] train-error:0.088700 [36] train-error:0.087485 [37] train-error:0.087485 [38] train-error:0.089915 [39] train-error:0.086270 [40] train-error:0.083840 [41] train-error:0.083840 [42] train-error:0.081409 [43] train-error:0.081409 [44] train-error:0.080194 [45] train-error:0.078979 [46] train-error:0.077764 [47] train-error:0.075334 [48] train-error:0.072904 [49] train-error:0.071689 [50] train-error:0.070474 [51] train-error:0.068044 [52] train-error:0.066829 [53] train-error:0.065614 [54] train-error:0.064399 [55] train-error:0.065614 [56] train-error:0.063183 [57] train-error:0.063183 [58] train-error:0.060753 [59] train-error:0.059538 [60] train-error:0.059538 [61] train-error:0.058323 [62] train-error:0.055893 [63] train-error:0.052248 [64] train-error:0.049818 [65] train-error:0.051033 [66] train-error:0.051033 [67] train-error:0.051033 [68] train-error:0.048603 [69] train-error:0.049818 [70] train-error:0.047388 [71] train-error:0.048603 [72] train-error:0.048603 [73] train-error:0.048603 [74] train-error:0.047388 [75] train-error:0.046173 [76] train-error:0.044957 [77] train-error:0.044957 [78] train-error:0.043742 [79] train-error:0.043742 [80] train-error:0.043742 [81] train-error:0.043742 [82] train-error:0.041312 [83] train-error:0.040097 [84] train-error:0.040097 [85] train-error:0.038882 [86] train-error:0.038882 [87] train-error:0.038882 [88] train-error:0.038882 [89] train-error:0.038882 [90] train-error:0.038882 [91] train-error:0.038882 [92] train-error:0.038882 [93] train-error:0.037667 [94] train-error:0.038882 [95] train-error:0.037667 [96] train-error:0.037667 [97] train-error:0.038882 [98] train-error:0.038882 [99] train-error:0.037667 [100] train-error:0.037667 [1] train-error:0.140948 [2] train-error:0.140948 [3] train-error:0.123937 [4] train-error:0.127582 [5] train-error:0.123937 [6] train-error:0.121507 [7] train-error:0.120292 [8] train-error:0.116646 [9] train-error:0.109356 [10] train-error:0.110571 [11] train-error:0.111786 [12] train-error:0.113001 [13] train-error:0.109356 [14] train-error:0.113001 [15] train-error:0.115431 [16] train-error:0.113001 [17] train-error:0.106926 [18] train-error:0.108141 [19] train-error:0.109356 [20] train-error:0.104496 [21] train-error:0.100851 [22] train-error:0.098420 [23] train-error:0.097205 [24] train-error:0.094775 [25] train-error:0.094775 [26] train-error:0.089915 [27] train-error:0.088700 [28] train-error:0.088700 [29] train-error:0.088700 [30] train-error:0.083840 [31] train-error:0.086270 [32] train-error:0.083840 [33] train-error:0.081409 [34] train-error:0.083840 [35] train-error:0.080194 [36] train-error:0.081409 [37] train-error:0.077764 [38] train-error:0.075334 [39] train-error:0.075334 [40] train-error:0.075334 [41] train-error:0.071689 [42] train-error:0.071689 [43] train-error:0.071689 [44] train-error:0.068044 [45] train-error:0.068044 [46] train-error:0.065614 [47] train-error:0.063183 [48] train-error:0.064399 [49] train-error:0.064399 [50] train-error:0.064399 [51] train-error:0.061968 [52] train-error:0.058323 [53] train-error:0.057108 [54] train-error:0.054678 [55] train-error:0.052248 [56] train-error:0.052248 [57] train-error:0.051033 [58] train-error:0.049818 [59] train-error:0.049818 [60] train-error:0.049818 [61] train-error:0.049818 [62] train-error:0.048603 [63] train-error:0.048603 [64] train-error:0.046173 [65] train-error:0.046173 [66] train-error:0.046173 [67] train-error:0.044957 [68] train-error:0.044957 [69] train-error:0.042527 [70] train-error:0.041312 [71] train-error:0.042527 [72] train-error:0.041312 [73] train-error:0.042527 [74] train-error:0.042527 [75] train-error:0.043742 [76] train-error:0.041312 [77] train-error:0.041312 [78] train-error:0.040097 [79] train-error:0.038882 [80] train-error:0.040097 [81] train-error:0.038882 [82] train-error:0.038882 [83] train-error:0.036452 [84] train-error:0.037667 [85] train-error:0.035237 [86] train-error:0.035237 [87] train-error:0.034022 [88] train-error:0.034022 [89] train-error:0.034022 [90] train-error:0.034022 [91] train-error:0.034022 [92] train-error:0.034022 [93] train-error:0.034022 [94] train-error:0.034022 [95] train-error:0.034022 [96] train-error:0.034022 [97] train-error:0.034022 [98] train-error:0.034022 [99] train-error:0.034022 [100] train-error:0.032807 [1] train-error:0.135036 [2] train-error:0.130170 [3] train-error:0.124088 [4] train-error:0.127737 [5] train-error:0.126521 [6] train-error:0.122871 [7] train-error:0.124088 [8] train-error:0.125304 [9] train-error:0.121655 [10] train-error:0.122871 [11] train-error:0.116788 [12] train-error:0.120438 [13] train-error:0.118005 [14] train-error:0.114355 [15] train-error:0.111922 [16] train-error:0.114355 [17] train-error:0.114355 [18] train-error:0.109489 [19] train-error:0.109489 [20] train-error:0.109489 [21] train-error:0.108273 [22] train-error:0.109489 [23] train-error:0.110706 [24] train-error:0.109489 [25] train-error:0.107056 [26] train-error:0.105839 [27] train-error:0.102190 [28] train-error:0.100973 [29] train-error:0.100973 [30] train-error:0.098540 [31] train-error:0.093674 [32] train-error:0.096107 [33] train-error:0.088808 [34] train-error:0.088808 [35] train-error:0.086375 [36] train-error:0.085158 [37] train-error:0.083942 [38] train-error:0.083942 [39] train-error:0.081509 [40] train-error:0.080292 [41] train-error:0.080292 [42] train-error:0.080292 [43] train-error:0.076642 [44] train-error:0.074209 [45] train-error:0.072993 [46] train-error:0.072993 [47] train-error:0.071776 [48] train-error:0.070560 [49] train-error:0.069343 [50] train-error:0.066910 [51] train-error:0.066910 [52] train-error:0.064477 [53] train-error:0.063260 [54] train-error:0.059611 [55] train-error:0.059611 [56] train-error:0.058394 [57] train-error:0.057178 [58] train-error:0.054745 [59] train-error:0.054745 [60] train-error:0.051095 [61] train-error:0.052311 [62] train-error:0.051095 [63] train-error:0.049878 [64] train-error:0.048662 [65] train-error:0.048662 [66] train-error:0.048662 [67] train-error:0.047445 [68] train-error:0.049878 [69] train-error:0.048662 [70] train-error:0.048662 [71] train-error:0.046229 [72] train-error:0.045012 [73] train-error:0.045012 [74] train-error:0.041363 [75] train-error:0.041363 [76] train-error:0.043796 [77] train-error:0.041363 [78] train-error:0.041363 [79] train-error:0.040146 [80] train-error:0.040146 [81] train-error:0.040146 [82] train-error:0.038929 [83] train-error:0.037713 [84] train-error:0.038929 [85] train-error:0.038929 [86] train-error:0.038929 [87] train-error:0.038929 [88] train-error:0.037713 [89] train-error:0.037713 [90] train-error:0.037713 [91] train-error:0.038929 [92] train-error:0.037713 [93] train-error:0.036496 [94] train-error:0.036496 [95] train-error:0.037713 [96] train-error:0.036496 [97] train-error:0.036496 [98] train-error:0.034063 [99] train-error:0.032847 [100] train-error:0.031630
[Tune-y] 2: acc.test.mean=0.8657637; time: 0.0 min [Tune-x] 3: max_depth=3; min_child_weight=5.66; subsample=0.752; colsample_bytree=0.959
[1] train-error:0.150852 [2] train-error:0.139903 [3] train-error:0.135036 [4] train-error:0.135036 [5] train-error:0.131387 [6] train-error:0.132603 [7] train-error:0.131387 [8] train-error:0.135036 [9] train-error:0.138686 [10] train-error:0.138686 [11] train-error:0.136253 [12] train-error:0.137470 [13] train-error:0.135036 [14] train-error:0.130170 [15] train-error:0.128954 [16] train-error:0.128954 [17] train-error:0.126521 [18] train-error:0.124088 [19] train-error:0.124088 [20] train-error:0.124088 [21] train-error:0.124088 [22] train-error:0.120438 [23] train-error:0.116788 [24] train-error:0.118005 [25] train-error:0.116788 [26] train-error:0.115572 [27] train-error:0.116788 [28] train-error:0.114355 [29] train-error:0.113139 [30] train-error:0.111922 [31] train-error:0.111922 [32] train-error:0.110706 [33] train-error:0.110706 [34] train-error:0.108273 [35] train-error:0.109489 [36] train-error:0.107056 [37] train-error:0.108273 [38] train-error:0.108273 [39] train-error:0.107056 [40] train-error:0.107056 [41] train-error:0.103406 [42] train-error:0.102190 [43] train-error:0.102190 [44] train-error:0.102190 [45] train-error:0.099757 [46] train-error:0.100973 [47] train-error:0.096107 [48] train-error:0.096107 [49] train-error:0.096107 [50] train-error:0.098540 [51] train-error:0.097324 [52] train-error:0.099757 [53] train-error:0.096107 [54] train-error:0.094891 [55] train-error:0.093674 [56] train-error:0.094891 [57] train-error:0.091241 [58] train-error:0.093674 [59] train-error:0.094891 [60] train-error:0.096107 [61] train-error:0.091241 [62] train-error:0.088808 [63] train-error:0.088808 [64] train-error:0.087591 [65] train-error:0.086375 [66] train-error:0.085158 [67] train-error:0.085158 [68] train-error:0.085158 [69] train-error:0.087591 [70] train-error:0.087591 [71] train-error:0.085158 [72] train-error:0.087591 [73] train-error:0.086375 [74] train-error:0.086375 [75] train-error:0.086375 [76] train-error:0.085158 [77] train-error:0.085158 [78] train-error:0.083942 [79] train-error:0.085158 [80] train-error:0.083942 [81] train-error:0.082725 [82] train-error:0.081509 [83] train-error:0.081509 [84] train-error:0.080292 [85] train-error:0.080292 [86] train-error:0.076642 [87] train-error:0.075426 [88] train-error:0.075426 [89] train-error:0.075426 [90] train-error:0.075426 [91] train-error:0.074209 [92] train-error:0.075426 [93] train-error:0.075426 [94] train-error:0.071776 [95] train-error:0.071776 [96] train-error:0.074209 [97] train-error:0.074209 [98] train-error:0.074209 [99] train-error:0.074209 [100] train-error:0.074209 [1] train-error:0.139903 [2] train-error:0.126521 [3] train-error:0.126521 [4] train-error:0.125304 [5] train-error:0.126521 [6] train-error:0.126521 [7] train-error:0.126521 [8] train-error:0.118005 [9] train-error:0.118005 [10] train-error:0.119221 [11] train-error:0.121655 [12] train-error:0.119221 [13] train-error:0.120438 [14] train-error:0.120438 [15] train-error:0.116788 [16] train-error:0.118005 [17] train-error:0.116788 [18] train-error:0.118005 [19] train-error:0.115572 [20] train-error:0.115572 [21] train-error:0.113139 [22] train-error:0.111922 [23] train-error:0.111922 [24] train-error:0.111922 [25] train-error:0.108273 [26] train-error:0.105839 [27] train-error:0.104623 [28] train-error:0.103406 [29] train-error:0.104623 [30] train-error:0.103406 [31] train-error:0.103406 [32] train-error:0.102190 [33] train-error:0.100973 [34] train-error:0.100973 [35] train-error:0.102190 [36] train-error:0.099757 [37] train-error:0.099757 [38] train-error:0.100973 [39] train-error:0.098540 [40] train-error:0.100973 [41] train-error:0.097324 [42] train-error:0.094891 [43] train-error:0.094891 [44] train-error:0.094891 [45] train-error:0.094891 [46] train-error:0.094891 [47] train-error:0.093674 [48] train-error:0.097324 [49] train-error:0.096107 [50] train-error:0.093674 [51] train-error:0.093674 [52] train-error:0.090024 [53] train-error:0.090024 [54] train-error:0.088808 [55] train-error:0.086375 [56] train-error:0.085158 [57] train-error:0.085158 [58] train-error:0.082725 [59] train-error:0.081509 [60] train-error:0.081509 [61] train-error:0.080292 [62] train-error:0.077859 [63] train-error:0.079075 [64] train-error:0.083942 [65] train-error:0.081509 [66] train-error:0.079075 [67] train-error:0.080292 [68] train-error:0.077859 [69] train-error:0.076642 [70] train-error:0.079075 [71] train-error:0.079075 [72] train-error:0.075426 [73] train-error:0.076642 [74] train-error:0.076642 [75] train-error:0.076642 [76] train-error:0.076642 [77] train-error:0.076642 [78] train-error:0.075426 [79] train-error:0.074209 [80] train-error:0.072993 [81] train-error:0.072993 [82] train-error:0.072993 [83] train-error:0.071776 [84] train-error:0.071776 [85] train-error:0.070560 [86] train-error:0.069343 [87] train-error:0.069343 [88] train-error:0.068127 [89] train-error:0.068127 [90] train-error:0.069343 [91] train-error:0.065693 [92] train-error:0.066910 [93] train-error:0.066910 [94] train-error:0.065693 [95] train-error:0.068127 [96] train-error:0.068127 [97] train-error:0.065693 [98] train-error:0.064477 [99] train-error:0.064477 [100] train-error:0.065693 [1] train-error:0.160389 [2] train-error:0.147023 [3] train-error:0.134872 [4] train-error:0.133657 [5] train-error:0.133657 [6] train-error:0.128797 [7] train-error:0.130012 [8] train-error:0.133657 [9] train-error:0.134872 [10] train-error:0.134872 [11] train-error:0.127582 [12] train-error:0.127582 [13] train-error:0.126367 [14] train-error:0.123937 [15] train-error:0.123937 [16] train-error:0.122722 [17] train-error:0.123937 [18] train-error:0.119077 [19] train-error:0.119077 [20] train-error:0.121507 [21] train-error:0.121507 [22] train-error:0.119077 [23] train-error:0.119077 [24] train-error:0.116646 [25] train-error:0.119077 [26] train-error:0.116646 [27] train-error:0.116646 [28] train-error:0.115431 [29] train-error:0.117861 [30] train-error:0.113001 [31] train-error:0.113001 [32] train-error:0.110571 [33] train-error:0.109356 [34] train-error:0.108141 [35] train-error:0.106926 [36] train-error:0.106926 [37] train-error:0.106926 [38] train-error:0.105711 [39] train-error:0.104496 [40] train-error:0.106926 [41] train-error:0.106926 [42] train-error:0.106926 [43] train-error:0.105711 [44] train-error:0.105711 [45] train-error:0.106926 [46] train-error:0.102066 [47] train-error:0.100851 [48] train-error:0.099635 [49] train-error:0.099635 [50] train-error:0.098420 [51] train-error:0.099635 [52] train-error:0.102066 [53] train-error:0.102066 [54] train-error:0.098420 [55] train-error:0.095990 [56] train-error:0.094775 [57] train-error:0.093560 [58] train-error:0.094775 [59] train-error:0.093560 [60] train-error:0.091130 [61] train-error:0.091130 [62] train-error:0.089915 [63] train-error:0.087485 [64] train-error:0.086270 [65] train-error:0.089915 [66] train-error:0.088700 [67] train-error:0.086270 [68] train-error:0.085055 [69] train-error:0.086270 [70] train-error:0.086270 [71] train-error:0.086270 [72] train-error:0.082625 [73] train-error:0.082625 [74] train-error:0.080194 [75] train-error:0.083840 [76] train-error:0.081409 [77] train-error:0.081409 [78] train-error:0.076549 [79] train-error:0.078979 [80] train-error:0.077764 [81] train-error:0.076549 [82] train-error:0.074119 [83] train-error:0.076549 [84] train-error:0.075334 [85] train-error:0.075334 [86] train-error:0.074119 [87] train-error:0.072904 [88] train-error:0.076549 [89] train-error:0.075334 [90] train-error:0.072904 [91] train-error:0.075334 [92] train-error:0.075334 [93] train-error:0.074119 [94] train-error:0.076549 [95] train-error:0.074119 [96] train-error:0.075334 [97] train-error:0.074119 [98] train-error:0.072904 [99] train-error:0.071689 [100] train-error:0.071689 [1] train-error:0.150668 [2] train-error:0.132442 [3] train-error:0.132442 [4] train-error:0.137303 [5] train-error:0.134872 [6] train-error:0.133657 [7] train-error:0.132442 [8] train-error:0.136087 [9] train-error:0.132442 [10] train-error:0.131227 [11] train-error:0.131227 [12] train-error:0.130012 [13] train-error:0.126367 [14] train-error:0.126367 [15] train-error:0.122722 [16] train-error:0.126367 [17] train-error:0.125152 [18] train-error:0.125152 [19] train-error:0.122722 [20] train-error:0.122722 [21] train-error:0.125152 [22] train-error:0.122722 [23] train-error:0.120292 [24] train-error:0.117861 [25] train-error:0.116646 [26] train-error:0.115431 [27] train-error:0.111786 [28] train-error:0.111786 [29] train-error:0.110571 [30] train-error:0.109356 [31] train-error:0.110571 [32] train-error:0.109356 [33] train-error:0.106926 [34] train-error:0.104496 [35] train-error:0.105711 [36] train-error:0.103281 [37] train-error:0.103281 [38] train-error:0.102066 [39] train-error:0.100851 [40] train-error:0.099635 [41] train-error:0.102066 [42] train-error:0.100851 [43] train-error:0.099635 [44] train-error:0.099635 [45] train-error:0.097205 [46] train-error:0.097205 [47] train-error:0.095990 [48] train-error:0.095990 [49] train-error:0.094775 [50] train-error:0.094775 [51] train-error:0.094775 [52] train-error:0.092345 [53] train-error:0.093560 [54] train-error:0.087485 [55] train-error:0.086270 [56] train-error:0.086270 [57] train-error:0.086270 [58] train-error:0.086270 [59] train-error:0.082625 [60] train-error:0.085055 [61] train-error:0.085055 [62] train-error:0.082625 [63] train-error:0.083840 [64] train-error:0.081409 [65] train-error:0.080194 [66] train-error:0.081409 [67] train-error:0.081409 [68] train-error:0.078979 [69] train-error:0.080194 [70] train-error:0.081409 [71] train-error:0.077764 [72] train-error:0.077764 [73] train-error:0.076549 [74] train-error:0.077764 [75] train-error:0.077764 [76] train-error:0.077764 [77] train-error:0.077764 [78] train-error:0.075334 [79] train-error:0.075334 [80] train-error:0.076549 [81] train-error:0.075334 [82] train-error:0.074119 [83] train-error:0.075334 [84] train-error:0.076549 [85] train-error:0.074119 [86] train-error:0.072904 [87] train-error:0.071689 [88] train-error:0.071689 [89] train-error:0.072904 [90] train-error:0.072904 [91] train-error:0.074119 [92] train-error:0.070474 [93] train-error:0.070474 [94] train-error:0.069259 [95] train-error:0.069259 [96] train-error:0.069259 [97] train-error:0.069259 [98] train-error:0.069259 [99] train-error:0.066829 [100] train-error:0.066829 [1] train-error:0.139903 [2] train-error:0.148418 [3] train-error:0.132603 [4] train-error:0.133820 [5] train-error:0.131387 [6] train-error:0.128954 [7] train-error:0.131387 [8] train-error:0.131387 [9] train-error:0.137470 [10] train-error:0.132603 [11] train-error:0.132603 [12] train-error:0.131387 [13] train-error:0.132603 [14] train-error:0.128954 [15] train-error:0.127737 [16] train-error:0.127737 [17] train-error:0.131387 [18] train-error:0.127737 [19] train-error:0.124088 [20] train-error:0.122871 [21] train-error:0.124088 [22] train-error:0.122871 [23] train-error:0.122871 [24] train-error:0.119221 [25] train-error:0.118005 [26] train-error:0.115572 [27] train-error:0.115572 [28] train-error:0.119221 [29] train-error:0.118005 [30] train-error:0.119221 [31] train-error:0.118005 [32] train-error:0.116788 [33] train-error:0.116788 [34] train-error:0.115572 [35] train-error:0.116788 [36] train-error:0.116788 [37] train-error:0.111922 [38] train-error:0.108273 [39] train-error:0.108273 [40] train-error:0.113139 [41] train-error:0.113139 [42] train-error:0.110706 [43] train-error:0.107056 [44] train-error:0.104623 [45] train-error:0.099757 [46] train-error:0.097324 [47] train-error:0.097324 [48] train-error:0.096107 [49] train-error:0.092457 [50] train-error:0.094891 [51] train-error:0.094891 [52] train-error:0.093674 [53] train-error:0.092457 [54] train-error:0.092457 [55] train-error:0.092457 [56] train-error:0.092457 [57] train-error:0.090024 [58] train-error:0.090024 [59] train-error:0.091241 [60] train-error:0.087591 [61] train-error:0.087591 [62] train-error:0.087591 [63] train-error:0.086375 [64] train-error:0.086375 [65] train-error:0.085158 [66] train-error:0.082725 [67] train-error:0.082725 [68] train-error:0.080292 [69] train-error:0.085158 [70] train-error:0.085158 [71] train-error:0.085158 [72] train-error:0.082725 [73] train-error:0.080292 [74] train-error:0.081509 [75] train-error:0.081509 [76] train-error:0.080292 [77] train-error:0.079075 [78] train-error:0.076642 [79] train-error:0.072993 [80] train-error:0.072993 [81] train-error:0.074209 [82] train-error:0.072993 [83] train-error:0.070560 [84] train-error:0.069343 [85] train-error:0.069343 [86] train-error:0.068127 [87] train-error:0.066910 [88] train-error:0.068127 [89] train-error:0.064477 [90] train-error:0.064477 [91] train-error:0.063260 [92] train-error:0.062044 [93] train-error:0.062044 [94] train-error:0.059611 [95] train-error:0.062044 [96] train-error:0.063260 [97] train-error:0.064477 [98] train-error:0.063260 [99] train-error:0.063260 [100] train-error:0.062044
[Tune-y] 3: acc.test.mean=0.8725598; time: 0.0 min [Tune-x] 4: max_depth=6; min_child_weight=3.5; subsample=0.914; colsample_bytree=0.725
[1] train-error:0.131387 [2] train-error:0.121655 [3] train-error:0.122871 [4] train-error:0.111922 [5] train-error:0.110706 [6] train-error:0.108273 [7] train-error:0.103406 [8] train-error:0.100973 [9] train-error:0.099757 [10] train-error:0.102190 [11] train-error:0.103406 [12] train-error:0.098540 [13] train-error:0.094891 [14] train-error:0.098540 [15] train-error:0.096107 [16] train-error:0.094891 [17] train-error:0.092457 [18] train-error:0.093674 [19] train-error:0.092457 [20] train-error:0.092457 [21] train-error:0.091241 [22] train-error:0.087591 [23] train-error:0.090024 [24] train-error:0.087591 [25] train-error:0.083942 [26] train-error:0.079075 [27] train-error:0.077859 [28] train-error:0.081509 [29] train-error:0.075426 [30] train-error:0.075426 [31] train-error:0.075426 [32] train-error:0.074209 [33] train-error:0.071776 [34] train-error:0.070560 [35] train-error:0.069343 [36] train-error:0.065693 [37] train-error:0.066910 [38] train-error:0.066910 [39] train-error:0.064477 [40] train-error:0.063260 [41] train-error:0.062044 [42] train-error:0.060827 [43] train-error:0.060827 [44] train-error:0.058394 [45] train-error:0.058394 [46] train-error:0.055961 [47] train-error:0.054745 [48] train-error:0.053528 [49] train-error:0.052311 [50] train-error:0.053528 [51] train-error:0.049878 [52] train-error:0.048662 [53] train-error:0.048662 [54] train-error:0.047445 [55] train-error:0.045012 [56] train-error:0.045012 [57] train-error:0.045012 [58] train-error:0.042579 [59] train-error:0.041363 [60] train-error:0.038929 [61] train-error:0.038929 [62] train-error:0.038929 [63] train-error:0.038929 [64] train-error:0.037713 [65] train-error:0.037713 [66] train-error:0.036496 [67] train-error:0.035280 [68] train-error:0.034063 [69] train-error:0.034063 [70] train-error:0.034063 [71] train-error:0.032847 [72] train-error:0.032847 [73] train-error:0.032847 [74] train-error:0.031630 [75] train-error:0.031630 [76] train-error:0.029197 [77] train-error:0.027981 [78] train-error:0.026764 [79] train-error:0.027981 [80] train-error:0.026764 [81] train-error:0.025547 [82] train-error:0.024331 [83] train-error:0.025547 [84] train-error:0.024331 [85] train-error:0.023114 [86] train-error:0.023114 [87] train-error:0.023114 [88] train-error:0.023114 [89] train-error:0.023114 [90] train-error:0.023114 [91] train-error:0.023114 [92] train-error:0.023114 [93] train-error:0.023114 [94] train-error:0.021898 [95] train-error:0.021898 [96] train-error:0.021898 [97] train-error:0.020681 [98] train-error:0.020681 [99] train-error:0.019465 [100] train-error:0.019465 [1] train-error:0.124088 [2] train-error:0.118005 [3] train-error:0.108273 [4] train-error:0.109489 [5] train-error:0.103406 [6] train-error:0.102190 [7] train-error:0.102190 [8] train-error:0.105839 [9] train-error:0.104623 [10] train-error:0.102190 [11] train-error:0.103406 [12] train-error:0.103406 [13] train-error:0.105839 [14] train-error:0.103406 [15] train-error:0.099757 [16] train-error:0.099757 [17] train-error:0.100973 [18] train-error:0.094891 [19] train-error:0.096107 [20] train-error:0.094891 [21] train-error:0.097324 [22] train-error:0.092457 [23] train-error:0.091241 [24] train-error:0.091241 [25] train-error:0.087591 [26] train-error:0.083942 [27] train-error:0.082725 [28] train-error:0.082725 [29] train-error:0.079075 [30] train-error:0.080292 [31] train-error:0.079075 [32] train-error:0.076642 [33] train-error:0.074209 [34] train-error:0.074209 [35] train-error:0.074209 [36] train-error:0.072993 [37] train-error:0.071776 [38] train-error:0.070560 [39] train-error:0.068127 [40] train-error:0.069343 [41] train-error:0.064477 [42] train-error:0.066910 [43] train-error:0.068127 [44] train-error:0.066910 [45] train-error:0.064477 [46] train-error:0.064477 [47] train-error:0.063260 [48] train-error:0.063260 [49] train-error:0.060827 [50] train-error:0.060827 [51] train-error:0.057178 [52] train-error:0.058394 [53] train-error:0.057178 [54] train-error:0.053528 [55] train-error:0.051095 [56] train-error:0.051095 [57] train-error:0.049878 [58] train-error:0.048662 [59] train-error:0.047445 [60] train-error:0.045012 [61] train-error:0.042579 [62] train-error:0.041363 [63] train-error:0.042579 [64] train-error:0.040146 [65] train-error:0.040146 [66] train-error:0.041363 [67] train-error:0.038929 [68] train-error:0.040146 [69] train-error:0.037713 [70] train-error:0.038929 [71] train-error:0.038929 [72] train-error:0.037713 [73] train-error:0.035280 [74] train-error:0.036496 [75] train-error:0.032847 [76] train-error:0.034063 [77] train-error:0.034063 [78] train-error:0.031630 [79] train-error:0.031630 [80] train-error:0.029197 [81] train-error:0.030414 [82] train-error:0.027981 [83] train-error:0.027981 [84] train-error:0.027981 [85] train-error:0.026764 [86] train-error:0.027981 [87] train-error:0.026764 [88] train-error:0.025547 [89] train-error:0.024331 [90] train-error:0.023114 [91] train-error:0.024331 [92] train-error:0.023114 [93] train-error:0.023114 [94] train-error:0.023114 [95] train-error:0.023114 [96] train-error:0.021898 [97] train-error:0.023114 [98] train-error:0.023114 [99] train-error:0.023114 [100] train-error:0.021898 [1] train-error:0.130012 [2] train-error:0.120292 [3] train-error:0.117861 [4] train-error:0.115431 [5] train-error:0.116646 [6] train-error:0.117861 [7] train-error:0.108141 [8] train-error:0.108141 [9] train-error:0.104496 [10] train-error:0.105711 [11] train-error:0.105711 [12] train-error:0.102066 [13] train-error:0.100851 [14] train-error:0.095990 [15] train-error:0.095990 [16] train-error:0.094775 [17] train-error:0.093560 [18] train-error:0.092345 [19] train-error:0.089915 [20] train-error:0.088700 [21] train-error:0.087485 [22] train-error:0.083840 [23] train-error:0.085055 [24] train-error:0.081409 [25] train-error:0.080194 [26] train-error:0.076549 [27] train-error:0.076549 [28] train-error:0.075334 [29] train-error:0.074119 [30] train-error:0.076549 [31] train-error:0.077764 [32] train-error:0.075334 [33] train-error:0.076549 [34] train-error:0.074119 [35] train-error:0.072904 [36] train-error:0.071689 [37] train-error:0.066829 [38] train-error:0.065614 [39] train-error:0.064399 [40] train-error:0.063183 [41] train-error:0.064399 [42] train-error:0.063183 [43] train-error:0.063183 [44] train-error:0.060753 [45] train-error:0.059538 [46] train-error:0.059538 [47] train-error:0.058323 [48] train-error:0.058323 [49] train-error:0.053463 [50] train-error:0.054678 [51] train-error:0.053463 [52] train-error:0.051033 [53] train-error:0.052248 [54] train-error:0.048603 [55] train-error:0.047388 [56] train-error:0.047388 [57] train-error:0.047388 [58] train-error:0.043742 [59] train-error:0.041312 [60] train-error:0.040097 [61] train-error:0.041312 [62] train-error:0.041312 [63] train-error:0.040097 [64] train-error:0.040097 [65] train-error:0.038882 [66] train-error:0.038882 [67] train-error:0.038882 [68] train-error:0.038882 [69] train-error:0.038882 [70] train-error:0.036452 [71] train-error:0.036452 [72] train-error:0.036452 [73] train-error:0.035237 [74] train-error:0.032807 [75] train-error:0.030377 [76] train-error:0.027947 [77] train-error:0.026731 [78] train-error:0.029162 [79] train-error:0.027947 [80] train-error:0.026731 [81] train-error:0.026731 [82] train-error:0.025516 [83] train-error:0.027947 [84] train-error:0.024301 [85] train-error:0.024301 [86] train-error:0.024301 [87] train-error:0.023086 [88] train-error:0.023086 [89] train-error:0.018226 [90] train-error:0.017011 [91] train-error:0.018226 [92] train-error:0.017011 [93] train-error:0.018226 [94] train-error:0.017011 [95] train-error:0.018226 [96] train-error:0.018226 [97] train-error:0.018226 [98] train-error:0.018226 [99] train-error:0.018226 [100] train-error:0.017011 [1] train-error:0.127582 [2] train-error:0.108141 [3] train-error:0.106926 [4] train-error:0.113001 [5] train-error:0.105711 [6] train-error:0.104496 [7] train-error:0.105711 [8] train-error:0.103281 [9] train-error:0.099635 [10] train-error:0.103281 [11] train-error:0.105711 [12] train-error:0.103281 [13] train-error:0.098420 [14] train-error:0.097205 [15] train-error:0.094775 [16] train-error:0.093560 [17] train-error:0.094775 [18] train-error:0.092345 [19] train-error:0.087485 [20] train-error:0.089915 [21] train-error:0.086270 [22] train-error:0.083840 [23] train-error:0.083840 [24] train-error:0.081409 [25] train-error:0.081409 [26] train-error:0.080194 [27] train-error:0.075334 [28] train-error:0.075334 [29] train-error:0.075334 [30] train-error:0.074119 [31] train-error:0.071689 [32] train-error:0.071689 [33] train-error:0.070474 [34] train-error:0.069259 [35] train-error:0.068044 [36] train-error:0.068044 [37] train-error:0.069259 [38] train-error:0.065614 [39] train-error:0.064399 [40] train-error:0.060753 [41] train-error:0.060753 [42] train-error:0.063183 [43] train-error:0.063183 [44] train-error:0.061968 [45] train-error:0.063183 [46] train-error:0.059538 [47] train-error:0.059538 [48] train-error:0.055893 [49] train-error:0.053463 [50] train-error:0.051033 [51] train-error:0.048603 [52] train-error:0.048603 [53] train-error:0.046173 [54] train-error:0.043742 [55] train-error:0.042527 [56] train-error:0.041312 [57] train-error:0.043742 [58] train-error:0.040097 [59] train-error:0.041312 [60] train-error:0.038882 [61] train-error:0.038882 [62] train-error:0.037667 [63] train-error:0.038882 [64] train-error:0.037667 [65] train-error:0.035237 [66] train-error:0.034022 [67] train-error:0.034022 [68] train-error:0.034022 [69] train-error:0.034022 [70] train-error:0.035237 [71] train-error:0.035237 [72] train-error:0.035237 [73] train-error:0.035237 [74] train-error:0.035237 [75] train-error:0.032807 [76] train-error:0.032807 [77] train-error:0.031592 [78] train-error:0.031592 [79] train-error:0.031592 [80] train-error:0.029162 [81] train-error:0.030377 [82] train-error:0.029162 [83] 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[22] train-error:0.098540 [23] train-error:0.098540 [24] train-error:0.094891 [25] train-error:0.096107 [26] train-error:0.094891 [27] train-error:0.093674 [28] train-error:0.087591 [29] train-error:0.086375 [30] train-error:0.081509 [31] train-error:0.081509 [32] train-error:0.079075 [33] train-error:0.075426 [34] train-error:0.072993 [35] train-error:0.072993 [36] train-error:0.072993 [37] train-error:0.072993 [38] train-error:0.071776 [39] train-error:0.068127 [40] train-error:0.069343 [41] train-error:0.066910 [42] train-error:0.065693 [43] train-error:0.064477 [44] train-error:0.065693 [45] train-error:0.062044 [46] train-error:0.060827 [47] train-error:0.063260 [48] train-error:0.058394 [49] train-error:0.054745 [50] train-error:0.055961 [51] train-error:0.053528 [52] train-error:0.051095 [53] train-error:0.052311 [54] train-error:0.052311 [55] train-error:0.049878 [56] train-error:0.048662 [57] train-error:0.047445 [58] train-error:0.047445 [59] train-error:0.047445 [60] train-error:0.046229 [61] train-error:0.047445 [62] train-error:0.045012 [63] train-error:0.043796 [64] train-error:0.043796 [65] train-error:0.045012 [66] train-error:0.043796 [67] train-error:0.041363 [68] train-error:0.040146 [69] train-error:0.041363 [70] train-error:0.038929 [71] train-error:0.038929 [72] train-error:0.037713 [73] train-error:0.037713 [74] train-error:0.037713 [75] train-error:0.036496 [76] train-error:0.035280 [77] train-error:0.031630 [78] train-error:0.032847 [79] train-error:0.031630 [80] train-error:0.030414 [81] train-error:0.030414 [82] train-error:0.030414 [83] train-error:0.029197 [84] train-error:0.029197 [85] train-error:0.027981 [86] train-error:0.027981 [87] train-error:0.029197 [88] train-error:0.026764 [89] train-error:0.026764 [90] train-error:0.025547 [91] train-error:0.026764 [92] train-error:0.024331 [93] train-error:0.024331 [94] train-error:0.024331 [95] train-error:0.024331 [96] train-error:0.024331 [97] train-error:0.023114 [98] train-error:0.024331 [99] train-error:0.021898 [100] train-error:0.023114
[Tune-y] 4: acc.test.mean=0.8706086; time: 0.0 min [Tune-x] 5: max_depth=5; min_child_weight=4.61; subsample=0.644; colsample_bytree=0.74
[1] train-error:0.143552 [2] train-error:0.145985 [3] train-error:0.143552 [4] train-error:0.141119 [5] train-error:0.139903 [6] train-error:0.133820 [7] train-error:0.130170 [8] train-error:0.133820 [9] train-error:0.131387 [10] train-error:0.132603 [11] train-error:0.126521 [12] train-error:0.124088 [13] train-error:0.127737 [14] train-error:0.125304 [15] train-error:0.120438 [16] train-error:0.121655 [17] train-error:0.122871 [18] train-error:0.120438 [19] train-error:0.118005 [20] train-error:0.121655 [21] train-error:0.120438 [22] train-error:0.115572 [23] train-error:0.115572 [24] train-error:0.113139 [25] train-error:0.113139 [26] train-error:0.111922 [27] train-error:0.109489 [28] train-error:0.108273 [29] train-error:0.108273 [30] train-error:0.107056 [31] train-error:0.110706 [32] train-error:0.109489 [33] train-error:0.107056 [34] train-error:0.105839 [35] train-error:0.105839 [36] train-error:0.103406 [37] train-error:0.099757 [38] train-error:0.098540 [39] train-error:0.096107 [40] train-error:0.092457 [41] train-error:0.091241 [42] train-error:0.091241 [43] train-error:0.090024 [44] train-error:0.090024 [45] train-error:0.086375 [46] train-error:0.087591 [47] train-error:0.086375 [48] train-error:0.086375 [49] train-error:0.082725 [50] train-error:0.082725 [51] train-error:0.082725 [52] train-error:0.082725 [53] train-error:0.081509 [54] train-error:0.082725 [55] train-error:0.079075 [56] train-error:0.080292 [57] train-error:0.077859 [58] train-error:0.077859 [59] train-error:0.077859 [60] train-error:0.079075 [61] train-error:0.076642 [62] train-error:0.076642 [63] train-error:0.077859 [64] train-error:0.077859 [65] train-error:0.076642 [66] train-error:0.074209 [67] train-error:0.072993 [68] train-error:0.072993 [69] train-error:0.072993 [70] train-error:0.070560 [71] train-error:0.068127 [72] train-error:0.063260 [73] train-error:0.063260 [74] train-error:0.060827 [75] train-error:0.062044 [76] train-error:0.063260 [77] train-error:0.060827 [78] train-error:0.059611 [79] train-error:0.057178 [80] train-error:0.057178 [81] train-error:0.055961 [82] train-error:0.053528 [83] train-error:0.054745 [84] train-error:0.053528 [85] train-error:0.054745 [86] train-error:0.051095 [87] train-error:0.051095 [88] train-error:0.052311 [89] train-error:0.053528 [90] train-error:0.052311 [91] train-error:0.052311 [92] train-error:0.053528 [93] train-error:0.053528 [94] train-error:0.053528 [95] train-error:0.053528 [96] train-error:0.053528 [97] train-error:0.053528 [98] train-error:0.051095 [99] train-error:0.051095 [100] train-error:0.048662 [1] train-error:0.133820 [2] train-error:0.133820 [3] train-error:0.122871 [4] train-error:0.126521 [5] train-error:0.126521 [6] train-error:0.124088 [7] train-error:0.120438 [8] train-error:0.114355 [9] train-error:0.120438 [10] train-error:0.116788 [11] train-error:0.119221 [12] train-error:0.118005 [13] train-error:0.118005 [14] train-error:0.118005 [15] train-error:0.118005 [16] train-error:0.119221 [17] train-error:0.115572 [18] train-error:0.113139 [19] train-error:0.109489 [20] train-error:0.109489 [21] train-error:0.110706 [22] train-error:0.108273 [23] train-error:0.105839 [24] train-error:0.108273 [25] train-error:0.107056 [26] train-error:0.105839 [27] train-error:0.107056 [28] train-error:0.103406 [29] train-error:0.103406 [30] train-error:0.104623 [31] train-error:0.103406 [32] train-error:0.102190 [33] train-error:0.098540 [34] train-error:0.098540 [35] train-error:0.097324 [36] train-error:0.097324 [37] train-error:0.094891 [38] train-error:0.094891 [39] train-error:0.092457 [40] train-error:0.093674 [41] train-error:0.091241 [42] train-error:0.092457 [43] train-error:0.091241 [44] train-error:0.087591 [45] train-error:0.085158 [46] train-error:0.085158 [47] train-error:0.086375 [48] train-error:0.086375 [49] train-error:0.088808 [50] train-error:0.085158 [51] train-error:0.083942 [52] train-error:0.085158 [53] train-error:0.082725 [54] train-error:0.081509 [55] train-error:0.079075 [56] train-error:0.081509 [57] train-error:0.081509 [58] train-error:0.080292 [59] train-error:0.081509 [60] train-error:0.077859 [61] train-error:0.077859 [62] train-error:0.077859 [63] train-error:0.077859 [64] train-error:0.080292 [65] train-error:0.080292 [66] train-error:0.079075 [67] train-error:0.079075 [68] train-error:0.077859 [69] train-error:0.080292 [70] train-error:0.079075 [71] train-error:0.077859 [72] train-error:0.072993 [73] train-error:0.070560 [74] train-error:0.070560 [75] train-error:0.070560 [76] train-error:0.070560 [77] train-error:0.066910 [78] train-error:0.066910 [79] train-error:0.068127 [80] train-error:0.066910 [81] train-error:0.065693 [82] train-error:0.066910 [83] train-error:0.064477 [84] train-error:0.064477 [85] train-error:0.064477 [86] train-error:0.062044 [87] train-error:0.062044 [88] train-error:0.062044 [89] train-error:0.060827 [90] train-error:0.062044 [91] train-error:0.060827 [92] 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[31] train-error:0.105711 [32] train-error:0.103281 [33] train-error:0.102066 [34] train-error:0.106926 [35] train-error:0.103281 [36] train-error:0.104496 [37] train-error:0.102066 [38] train-error:0.102066 [39] train-error:0.094775 [40] train-error:0.094775 [41] train-error:0.093560 [42] train-error:0.091130 [43] train-error:0.091130 [44] train-error:0.089915 [45] train-error:0.089915 [46] train-error:0.091130 [47] train-error:0.087485 [48] train-error:0.086270 [49] train-error:0.086270 [50] train-error:0.085055 [51] train-error:0.082625 [52] train-error:0.082625 [53] train-error:0.082625 [54] train-error:0.083840 [55] train-error:0.081409 [56] train-error:0.077764 [57] train-error:0.078979 [58] train-error:0.077764 [59] train-error:0.075334 [60] train-error:0.072904 [61] train-error:0.075334 [62] train-error:0.074119 [63] train-error:0.071689 [64] train-error:0.072904 [65] train-error:0.075334 [66] train-error:0.072904 [67] train-error:0.072904 [68] train-error:0.072904 [69] train-error:0.069259 [70] train-error:0.070474 [71] train-error:0.069259 [72] train-error:0.068044 [73] train-error:0.069259 [74] train-error:0.068044 [75] train-error:0.068044 [76] train-error:0.069259 [77] train-error:0.069259 [78] train-error:0.066829 [79] train-error:0.066829 [80] train-error:0.064399 [81] train-error:0.063183 [82] train-error:0.064399 [83] train-error:0.061968 [84] train-error:0.061968 [85] train-error:0.061968 [86] train-error:0.060753 [87] train-error:0.060753 [88] train-error:0.060753 [89] train-error:0.061968 [90] train-error:0.061968 [91] train-error:0.061968 [92] train-error:0.060753 [93] train-error:0.060753 [94] train-error:0.059538 [95] train-error:0.058323 [96] train-error:0.055893 [97] train-error:0.054678 [98] train-error:0.054678 [99] train-error:0.052248 [100] train-error:0.052248 [1] train-error:0.143378 [2] train-error:0.136087 [3] train-error:0.130012 [4] train-error:0.125152 [5] train-error:0.132442 [6] train-error:0.132442 [7] train-error:0.127582 [8] train-error:0.126367 [9] train-error:0.130012 [10] train-error:0.126367 [11] train-error:0.128797 [12] train-error:0.125152 [13] train-error:0.126367 [14] train-error:0.125152 [15] train-error:0.122722 [16] train-error:0.120292 [17] train-error:0.119077 [18] train-error:0.117861 [19] train-error:0.115431 [20] train-error:0.111786 [21] train-error:0.113001 [22] train-error:0.115431 [23] train-error:0.113001 [24] train-error:0.110571 [25] train-error:0.110571 [26] train-error:0.108141 [27] train-error:0.109356 [28] train-error:0.105711 [29] train-error:0.105711 [30] train-error:0.102066 [31] train-error:0.104496 [32] train-error:0.102066 [33] train-error:0.098420 [34] train-error:0.098420 [35] train-error:0.099635 [36] train-error:0.097205 [37] train-error:0.095990 [38] train-error:0.092345 [39] train-error:0.089915 [40] train-error:0.087485 [41] train-error:0.088700 [42] train-error:0.085055 [43] train-error:0.085055 [44] train-error:0.085055 [45] train-error:0.085055 [46] train-error:0.083840 [47] train-error:0.083840 [48] train-error:0.085055 [49] train-error:0.078979 [50] train-error:0.080194 [51] train-error:0.078979 [52] train-error:0.078979 [53] train-error:0.078979 [54] train-error:0.078979 [55] train-error:0.076549 [56] train-error:0.080194 [57] train-error:0.076549 [58] train-error:0.076549 [59] train-error:0.077764 [60] train-error:0.075334 [61] train-error:0.075334 [62] train-error:0.072904 [63] train-error:0.074119 [64] train-error:0.071689 [65] train-error:0.072904 [66] train-error:0.072904 [67] train-error:0.071689 [68] train-error:0.072904 [69] train-error:0.066829 [70] train-error:0.068044 [71] train-error:0.069259 [72] train-error:0.068044 [73] train-error:0.063183 [74] train-error:0.061968 [75] train-error:0.059538 [76] train-error:0.059538 [77] train-error:0.059538 [78] train-error:0.057108 [79] train-error:0.055893 [80] train-error:0.057108 [81] train-error:0.055893 [82] train-error:0.055893 [83] train-error:0.054678 [84] train-error:0.054678 [85] train-error:0.054678 [86] train-error:0.057108 [87] train-error:0.055893 [88] train-error:0.054678 [89] train-error:0.052248 [90] train-error:0.051033 [91] train-error:0.049818 [92] train-error:0.047388 [93] train-error:0.047388 [94] train-error:0.047388 [95] train-error:0.048603 [96] train-error:0.047388 [97] train-error:0.044957 [98] train-error:0.043742 [99] train-error:0.044957 [100] train-error:0.042527 [1] train-error:0.163017 [2] train-error:0.143552 [3] train-error:0.136253 [4] train-error:0.138686 [5] train-error:0.135036 [6] train-error:0.131387 [7] train-error:0.125304 [8] train-error:0.127737 [9] train-error:0.130170 [10] train-error:0.125304 [11] train-error:0.126521 [12] train-error:0.125304 [13] train-error:0.120438 [14] train-error:0.122871 [15] train-error:0.126521 [16] train-error:0.122871 [17] train-error:0.119221 [18] train-error:0.118005 [19] train-error:0.118005 [20] train-error:0.118005 [21] train-error:0.119221 [22] train-error:0.119221 [23] train-error:0.118005 [24] train-error:0.118005 [25] train-error:0.118005 [26] train-error:0.115572 [27] train-error:0.115572 [28] train-error:0.114355 [29] train-error:0.110706 [30] train-error:0.110706 [31] train-error:0.109489 [32] train-error:0.109489 [33] train-error:0.109489 [34] train-error:0.108273 [35] train-error:0.108273 [36] train-error:0.105839 [37] train-error:0.100973 [38] train-error:0.099757 [39] train-error:0.100973 [40] train-error:0.099757 [41] train-error:0.099757 [42] train-error:0.098540 [43] train-error:0.094891 [44] train-error:0.090024 [45] train-error:0.092457 [46] train-error:0.088808 [47] train-error:0.088808 [48] train-error:0.087591 [49] train-error:0.085158 [50] train-error:0.085158 [51] train-error:0.081509 [52] train-error:0.081509 [53] train-error:0.079075 [54] train-error:0.079075 [55] train-error:0.079075 [56] train-error:0.076642 [57] train-error:0.075426 [58] train-error:0.075426 [59] train-error:0.074209 [60] train-error:0.075426 [61] train-error:0.072993 [62] train-error:0.075426 [63] train-error:0.074209 [64] train-error:0.075426 [65] train-error:0.074209 [66] train-error:0.074209 [67] train-error:0.071776 [68] train-error:0.070560 [69] train-error:0.070560 [70] train-error:0.066910 [71] train-error:0.065693 [72] train-error:0.068127 [73] train-error:0.066910 [74] train-error:0.063260 [75] train-error:0.064477 [76] train-error:0.064477 [77] train-error:0.064477 [78] train-error:0.064477 [79] train-error:0.060827 [80] train-error:0.062044 [81] train-error:0.062044 [82] train-error:0.062044 [83] train-error:0.059611 [84] train-error:0.058394 [85] train-error:0.059611 [86] train-error:0.060827 [87] train-error:0.059611 [88] train-error:0.059611 [89] train-error:0.060827 [90] train-error:0.059611 [91] train-error:0.059611 [92] train-error:0.059611 [93] train-error:0.060827 [94] train-error:0.059611 [95] train-error:0.059611 [96] train-error:0.058394 [97] train-error:0.057178 [98] train-error:0.055961 [99] train-error:0.055961 [100] train-error:0.054745
[Tune-y] 5: acc.test.mean=0.8754914; time: 0.0 min [Tune-x] 6: max_depth=3; min_child_weight=5.71; subsample=0.631; colsample_bytree=0.766
[1] train-error:0.171533 [2] train-error:0.148418 [3] train-error:0.148418 [4] train-error:0.148418 [5] train-error:0.148418 [6] train-error:0.148418 [7] train-error:0.148418 [8] train-error:0.141119 [9] train-error:0.138686 [10] train-error:0.143552 [11] train-error:0.139903 [12] train-error:0.141119 [13] train-error:0.143552 [14] train-error:0.141119 [15] train-error:0.139903 [16] train-error:0.135036 [17] train-error:0.130170 [18] train-error:0.131387 [19] train-error:0.131387 [20] train-error:0.132603 [21] train-error:0.131387 [22] train-error:0.130170 [23] train-error:0.130170 [24] train-error:0.128954 [25] train-error:0.128954 [26] train-error:0.125304 [27] train-error:0.121655 [28] train-error:0.124088 [29] train-error:0.120438 [30] train-error:0.120438 [31] train-error:0.116788 [32] train-error:0.114355 [33] train-error:0.115572 [34] train-error:0.111922 [35] train-error:0.113139 [36] train-error:0.109489 [37] train-error:0.114355 [38] train-error:0.110706 [39] train-error:0.107056 [40] train-error:0.110706 [41] train-error:0.108273 [42] train-error:0.107056 [43] train-error:0.107056 [44] train-error:0.108273 [45] train-error:0.105839 [46] train-error:0.104623 [47] train-error:0.103406 [48] train-error:0.103406 [49] train-error:0.102190 [50] train-error:0.099757 [51] train-error:0.099757 [52] train-error:0.099757 [53] train-error:0.099757 [54] train-error:0.099757 [55] train-error:0.099757 [56] train-error:0.099757 [57] train-error:0.097324 [58] train-error:0.093674 [59] train-error:0.096107 [60] train-error:0.097324 [61] train-error:0.097324 [62] train-error:0.096107 [63] train-error:0.096107 [64] train-error:0.093674 [65] train-error:0.093674 [66] train-error:0.092457 [67] train-error:0.090024 [68] train-error:0.090024 [69] train-error:0.088808 [70] train-error:0.092457 [71] train-error:0.090024 [72] train-error:0.087591 [73] train-error:0.088808 [74] train-error:0.088808 [75] train-error:0.086375 [76] train-error:0.086375 [77] train-error:0.088808 [78] train-error:0.088808 [79] train-error:0.087591 [80] train-error:0.088808 [81] train-error:0.087591 [82] train-error:0.085158 [83] train-error:0.087591 [84] train-error:0.083942 [85] train-error:0.085158 [86] train-error:0.083942 [87] train-error:0.083942 [88] train-error:0.083942 [89] train-error:0.081509 [90] train-error:0.080292 [91] train-error:0.081509 [92] train-error:0.081509 [93] train-error:0.081509 [94] train-error:0.081509 [95] train-error:0.080292 [96] train-error:0.081509 [97] train-error:0.079075 [98] train-error:0.079075 [99] train-error:0.079075 [100] train-error:0.076642 [1] train-error:0.136253 [2] train-error:0.141119 [3] train-error:0.136253 [4] train-error:0.145985 [5] train-error:0.130170 [6] train-error:0.128954 [7] train-error:0.128954 [8] train-error:0.128954 [9] train-error:0.130170 [10] train-error:0.131387 [11] train-error:0.122871 [12] train-error:0.128954 [13] train-error:0.126521 [14] train-error:0.127737 [15] train-error:0.124088 [16] train-error:0.124088 [17] train-error:0.122871 [18] train-error:0.124088 [19] train-error:0.124088 [20] train-error:0.120438 [21] train-error:0.119221 [22] train-error:0.114355 [23] train-error:0.109489 [24] train-error:0.110706 [25] train-error:0.111922 [26] train-error:0.109489 [27] train-error:0.111922 [28] train-error:0.110706 [29] train-error:0.110706 [30] train-error:0.111922 [31] train-error:0.114355 [32] train-error:0.108273 [33] train-error:0.113139 [34] train-error:0.110706 [35] train-error:0.108273 [36] train-error:0.107056 [37] train-error:0.107056 [38] train-error:0.104623 [39] train-error:0.103406 [40] train-error:0.102190 [41] train-error:0.100973 [42] train-error:0.099757 [43] train-error:0.102190 [44] train-error:0.100973 [45] train-error:0.100973 [46] train-error:0.098540 [47] train-error:0.096107 [48] train-error:0.096107 [49] train-error:0.097324 [50] train-error:0.096107 [51] train-error:0.093674 [52] train-error:0.092457 [53] train-error:0.093674 [54] train-error:0.092457 [55] train-error:0.092457 [56] train-error:0.088808 [57] train-error:0.088808 [58] train-error:0.090024 [59] train-error:0.090024 [60] train-error:0.086375 [61] train-error:0.086375 [62] train-error:0.085158 [63] train-error:0.086375 [64] train-error:0.085158 [65] train-error:0.085158 [66] train-error:0.083942 [67] train-error:0.085158 [68] train-error:0.083942 [69] train-error:0.081509 [70] train-error:0.081509 [71] train-error:0.081509 [72] train-error:0.081509 [73] train-error:0.082725 [74] train-error:0.081509 [75] train-error:0.080292 [76] train-error:0.080292 [77] train-error:0.079075 [78] train-error:0.080292 [79] train-error:0.079075 [80] train-error:0.081509 [81] train-error:0.081509 [82] train-error:0.081509 [83] train-error:0.081509 [84] train-error:0.080292 [85] train-error:0.080292 [86] train-error:0.080292 [87] train-error:0.080292 [88] train-error:0.080292 [89] train-error:0.079075 [90] train-error:0.077859 [91] train-error:0.077859 [92] train-error:0.079075 [93] train-error:0.079075 [94] train-error:0.077859 [95] train-error:0.077859 [96] train-error:0.077859 [97] train-error:0.077859 [98] train-error:0.077859 [99] train-error:0.076642 [100] train-error:0.075426 [1] train-error:0.155529 [2] train-error:0.140948 [3] train-error:0.140948 [4] train-error:0.140948 [5] train-error:0.145808 [6] train-error:0.147023 [7] train-error:0.138518 [8] train-error:0.136087 [9] train-error:0.138518 [10] train-error:0.136087 [11] train-error:0.136087 [12] train-error:0.133657 [13] train-error:0.131227 [14] train-error:0.132442 [15] train-error:0.130012 [16] train-error:0.128797 [17] train-error:0.126367 [18] train-error:0.127582 [19] train-error:0.127582 [20] train-error:0.127582 [21] train-error:0.127582 [22] train-error:0.126367 [23] train-error:0.126367 [24] train-error:0.125152 [25] train-error:0.123937 [26] train-error:0.122722 [27] train-error:0.122722 [28] train-error:0.122722 [29] train-error:0.120292 [30] train-error:0.120292 [31] train-error:0.119077 [32] train-error:0.119077 [33] train-error:0.117861 [34] train-error:0.117861 [35] train-error:0.117861 [36] train-error:0.116646 [37] train-error:0.115431 [38] train-error:0.115431 [39] train-error:0.115431 [40] train-error:0.116646 [41] train-error:0.113001 [42] train-error:0.110571 [43] train-error:0.109356 [44] train-error:0.109356 [45] train-error:0.110571 [46] train-error:0.108141 [47] train-error:0.106926 [48] train-error:0.105711 [49] train-error:0.104496 [50] train-error:0.104496 [51] train-error:0.105711 [52] train-error:0.102066 [53] train-error:0.104496 [54] train-error:0.102066 [55] train-error:0.100851 [56] train-error:0.098420 [57] train-error:0.099635 [58] train-error:0.099635 [59] train-error:0.097205 [60] train-error:0.098420 [61] train-error:0.097205 [62] train-error:0.095990 [63] train-error:0.092345 [64] train-error:0.091130 [65] train-error:0.092345 [66] train-error:0.092345 [67] train-error:0.093560 [68] train-error:0.094775 [69] train-error:0.094775 [70] train-error:0.093560 [71] train-error:0.091130 [72] train-error:0.092345 [73] train-error:0.092345 [74] train-error:0.092345 [75] train-error:0.091130 [76] train-error:0.091130 [77] train-error:0.088700 [78] train-error:0.088700 [79] train-error:0.087485 [80] train-error:0.087485 [81] train-error:0.088700 [82] train-error:0.088700 [83] train-error:0.089915 [84] train-error:0.085055 [85] train-error:0.086270 [86] train-error:0.087485 [87] train-error:0.085055 [88] train-error:0.087485 [89] train-error:0.086270 [90] train-error:0.086270 [91] train-error:0.086270 [92] train-error:0.085055 [93] train-error:0.082625 [94] train-error:0.082625 [95] train-error:0.082625 [96] train-error:0.082625 [97] train-error:0.085055 [98] train-error:0.078979 [99] train-error:0.078979 [100] train-error:0.078979 [1] train-error:0.161604 [2] train-error:0.139733 [3] train-error:0.143378 [4] train-error:0.131227 [5] train-error:0.132442 [6] train-error:0.132442 [7] train-error:0.134872 [8] train-error:0.136087 [9] train-error:0.132442 [10] train-error:0.137303 [11] train-error:0.131227 [12] train-error:0.132442 [13] train-error:0.131227 [14] train-error:0.131227 [15] train-error:0.132442 [16] train-error:0.130012 [17] train-error:0.130012 [18] train-error:0.127582 [19] train-error:0.127582 [20] train-error:0.127582 [21] train-error:0.128797 [22] train-error:0.126367 [23] train-error:0.122722 [24] train-error:0.121507 [25] train-error:0.117861 [26] train-error:0.123937 [27] train-error:0.123937 [28] train-error:0.117861 [29] train-error:0.117861 [30] train-error:0.114216 [31] train-error:0.110571 [32] train-error:0.110571 [33] train-error:0.105711 [34] train-error:0.104496 [35] train-error:0.108141 [36] train-error:0.106926 [37] train-error:0.106926 [38] train-error:0.105711 [39] train-error:0.103281 [40] train-error:0.102066 [41] train-error:0.100851 [42] train-error:0.094775 [43] train-error:0.093560 [44] train-error:0.094775 [45] train-error:0.095990 [46] train-error:0.089915 [47] train-error:0.089915 [48] train-error:0.091130 [49] train-error:0.088700 [50] train-error:0.088700 [51] train-error:0.087485 [52] train-error:0.089915 [53] train-error:0.089915 [54] train-error:0.088700 [55] train-error:0.086270 [56] train-error:0.088700 [57] train-error:0.087485 [58] train-error:0.083840 [59] train-error:0.086270 [60] train-error:0.083840 [61] train-error:0.083840 [62] train-error:0.083840 [63] train-error:0.082625 [64] train-error:0.082625 [65] train-error:0.082625 [66] train-error:0.082625 [67] train-error:0.083840 [68] train-error:0.083840 [69] train-error:0.082625 [70] train-error:0.082625 [71] train-error:0.085055 [72] train-error:0.083840 [73] train-error:0.083840 [74] train-error:0.085055 [75] train-error:0.082625 [76] train-error:0.081409 [77] train-error:0.078979 [78] train-error:0.078979 [79] train-error:0.081409 [80] train-error:0.080194 [81] train-error:0.078979 [82] train-error:0.077764 [83] train-error:0.076549 [84] train-error:0.076549 [85] train-error:0.076549 [86] train-error:0.076549 [87] train-error:0.076549 [88] train-error:0.076549 [89] train-error:0.076549 [90] train-error:0.076549 [91] train-error:0.077764 [92] train-error:0.076549 [93] train-error:0.077764 [94] train-error:0.078979 [95] train-error:0.075334 [96] train-error:0.075334 [97] train-error:0.076549 [98] train-error:0.076549 [99] train-error:0.074119 [100] train-error:0.071689 [1] train-error:0.141119 [2] train-error:0.141119 [3] train-error:0.142336 [4] train-error:0.142336 [5] train-error:0.141119 [6] train-error:0.142336 [7] train-error:0.141119 [8] train-error:0.136253 [9] train-error:0.136253 [10] train-error:0.135036 [11] train-error:0.138686 [12] train-error:0.133820 [13] train-error:0.135036 [14] train-error:0.133820 [15] train-error:0.135036 [16] train-error:0.130170 [17] train-error:0.130170 [18] train-error:0.128954 [19] train-error:0.130170 [20] train-error:0.127737 [21] train-error:0.128954 [22] train-error:0.128954 [23] train-error:0.127737 [24] train-error:0.122871 [25] train-error:0.124088 [26] train-error:0.121655 [27] train-error:0.122871 [28] train-error:0.121655 [29] train-error:0.120438 [30] train-error:0.122871 [31] train-error:0.124088 [32] train-error:0.118005 [33] train-error:0.118005 [34] train-error:0.118005 [35] train-error:0.118005 [36] train-error:0.115572 [37] train-error:0.115572 [38] train-error:0.114355 [39] train-error:0.111922 [40] train-error:0.113139 [41] train-error:0.110706 [42] train-error:0.109489 [43] train-error:0.111922 [44] train-error:0.107056 [45] train-error:0.108273 [46] train-error:0.103406 [47] train-error:0.107056 [48] train-error:0.105839 [49] train-error:0.102190 [50] train-error:0.102190 [51] train-error:0.100973 [52] train-error:0.099757 [53] train-error:0.098540 [54] train-error:0.100973 [55] train-error:0.098540 [56] train-error:0.098540 [57] train-error:0.098540 [58] train-error:0.099757 [59] train-error:0.097324 [60] train-error:0.098540 [61] train-error:0.097324 [62] train-error:0.097324 [63] train-error:0.094891 [64] train-error:0.094891 [65] train-error:0.094891 [66] train-error:0.092457 [67] train-error:0.092457 [68] train-error:0.090024 [69] train-error:0.090024 [70] train-error:0.087591 [71] train-error:0.086375 [72] train-error:0.086375 [73] train-error:0.085158 [74] train-error:0.086375 [75] train-error:0.085158 [76] train-error:0.085158 [77] train-error:0.083942 [78] train-error:0.082725 [79] train-error:0.082725 [80] train-error:0.083942 [81] train-error:0.082725 [82] train-error:0.081509 [83] train-error:0.080292 [84] train-error:0.082725 [85] train-error:0.080292 [86] train-error:0.081509 [87] train-error:0.081509 [88] train-error:0.082725 [89] train-error:0.082725 [90] train-error:0.082725 [91] train-error:0.081509 [92] train-error:0.080292 [93] train-error:0.079075 [94] train-error:0.080292 [95] train-error:0.080292 [96] train-error:0.077859 [97] train-error:0.079075 [98] train-error:0.079075 [99] train-error:0.077859 [100] train-error:0.079075
[Tune-y] 6: acc.test.mean=0.8686621; time: 0.0 min [Tune-x] 7: max_depth=10; min_child_weight=3.05; subsample=0.955; colsample_bytree=0.829
[1] train-error:0.124088 [2] train-error:0.114355 [3] train-error:0.111922 [4] train-error:0.104623 [5] train-error:0.100973 [6] train-error:0.098540 [7] train-error:0.094891 [8] train-error:0.098540 [9] train-error:0.096107 [10] train-error:0.091241 [11] train-error:0.092457 [12] train-error:0.087591 [13] train-error:0.086375 [14] train-error:0.085158 [15] train-error:0.080292 [16] train-error:0.077859 [17] train-error:0.077859 [18] train-error:0.077859 [19] train-error:0.075426 [20] train-error:0.075426 [21] train-error:0.071776 [22] train-error:0.069343 [23] train-error:0.068127 [24] train-error:0.069343 [25] train-error:0.065693 [26] train-error:0.064477 [27] train-error:0.065693 [28] train-error:0.064477 [29] train-error:0.063260 [30] train-error:0.062044 [31] train-error:0.059611 [32] train-error:0.055961 [33] train-error:0.054745 [34] train-error:0.055961 [35] train-error:0.054745 [36] train-error:0.052311 [37] train-error:0.049878 [38] train-error:0.047445 [39] train-error:0.045012 [40] train-error:0.043796 [41] train-error:0.042579 [42] train-error:0.043796 [43] train-error:0.038929 [44] train-error:0.040146 [45] train-error:0.038929 [46] train-error:0.037713 [47] train-error:0.038929 [48] train-error:0.037713 [49] train-error:0.037713 [50] train-error:0.035280 [51] train-error:0.035280 [52] train-error:0.035280 [53] train-error:0.031630 [54] train-error:0.031630 [55] train-error:0.029197 [56] train-error:0.027981 [57] train-error:0.027981 [58] train-error:0.026764 [59] train-error:0.026764 [60] train-error:0.024331 [61] train-error:0.024331 [62] train-error:0.021898 [63] train-error:0.023114 [64] train-error:0.020681 [65] train-error:0.020681 [66] train-error:0.019465 [67] train-error:0.019465 [68] train-error:0.019465 [69] train-error:0.020681 [70] train-error:0.020681 [71] train-error:0.020681 [72] train-error:0.020681 [73] train-error:0.019465 [74] train-error:0.019465 [75] train-error:0.018248 [76] train-error:0.018248 [77] train-error:0.018248 [78] train-error:0.017032 [79] train-error:0.017032 [80] train-error:0.017032 [81] train-error:0.017032 [82] train-error:0.017032 [83] train-error:0.017032 [84] train-error:0.017032 [85] train-error:0.017032 [86] train-error:0.014599 [87] train-error:0.014599 [88] train-error:0.013382 [89] train-error:0.013382 [90] train-error:0.012165 [91] train-error:0.012165 [92] train-error:0.012165 [93] train-error:0.010949 [94] train-error:0.009732 [95] train-error:0.010949 [96] train-error:0.010949 [97] train-error:0.009732 [98] train-error:0.009732 [99] train-error:0.008516 [100] train-error:0.008516 [1] train-error:0.118005 [2] train-error:0.105839 [3] train-error:0.108273 [4] train-error:0.105839 [5] train-error:0.100973 [6] train-error:0.098540 [7] train-error:0.094891 [8] train-error:0.090024 [9] train-error:0.091241 [10] train-error:0.088808 [11] train-error:0.085158 [12] train-error:0.085158 [13] train-error:0.085158 [14] train-error:0.083942 [15] train-error:0.085158 [16] train-error:0.086375 [17] train-error:0.082725 [18] train-error:0.080292 [19] train-error:0.079075 [20] train-error:0.076642 [21] train-error:0.075426 [22] train-error:0.072993 [23] train-error:0.071776 [24] train-error:0.066910 [25] train-error:0.068127 [26] train-error:0.065693 [27] train-error:0.063260 [28] train-error:0.062044 [29] train-error:0.062044 [30] train-error:0.058394 [31] train-error:0.057178 [32] train-error:0.058394 [33] train-error:0.057178 [34] train-error:0.057178 [35] train-error:0.057178 [36] train-error:0.054745 [37] train-error:0.053528 [38] train-error:0.053528 [39] train-error:0.051095 [40] train-error:0.052311 [41] train-error:0.049878 [42] train-error:0.049878 [43] train-error:0.048662 [44] train-error:0.049878 [45] train-error:0.049878 [46] train-error:0.041363 [47] train-error:0.042579 [48] train-error:0.041363 [49] train-error:0.040146 [50] train-error:0.041363 [51] train-error:0.042579 [52] train-error:0.041363 [53] train-error:0.037713 [54] train-error:0.036496 [55] train-error:0.037713 [56] train-error:0.034063 [57] train-error:0.034063 [58] train-error:0.034063 [59] train-error:0.025547 [60] train-error:0.025547 [61] train-error:0.024331 [62] train-error:0.025547 [63] train-error:0.025547 [64] train-error:0.021898 [65] train-error:0.020681 [66] train-error:0.020681 [67] train-error:0.020681 [68] train-error:0.017032 [69] train-error:0.018248 [70] train-error:0.015815 [71] train-error:0.018248 [72] train-error:0.017032 [73] train-error:0.015815 [74] train-error:0.015815 [75] train-error:0.013382 [76] train-error:0.013382 [77] train-error:0.013382 [78] train-error:0.013382 [79] train-error:0.014599 [80] train-error:0.012165 [81] train-error:0.010949 [82] train-error:0.010949 [83] train-error:0.010949 [84] train-error:0.009732 [85] train-error:0.010949 [86] train-error:0.010949 [87] train-error:0.012165 [88] train-error:0.010949 [89] train-error:0.009732 [90] train-error:0.009732 [91] train-error:0.009732 [92] train-error:0.008516 [93] train-error:0.008516 [94] train-error:0.008516 [95] train-error:0.008516 [96] train-error:0.008516 [97] train-error:0.007299 [98] train-error:0.007299 [99] train-error:0.006083 [100] train-error:0.006083 [1] train-error:0.117861 [2] train-error:0.111786 [3] train-error:0.103281 [4] train-error:0.092345 [5] train-error:0.095990 [6] train-error:0.094775 [7] train-error:0.091130 [8] train-error:0.093560 [9] train-error:0.089915 [10] train-error:0.091130 [11] train-error:0.093560 [12] train-error:0.091130 [13] train-error:0.088700 [14] train-error:0.085055 [15] train-error:0.086270 [16] train-error:0.086270 [17] train-error:0.085055 [18] train-error:0.085055 [19] train-error:0.081409 [20] train-error:0.081409 [21] train-error:0.077764 [22] train-error:0.074119 [23] train-error:0.072904 [24] train-error:0.072904 [25] train-error:0.072904 [26] train-error:0.064399 [27] train-error:0.063183 [28] train-error:0.060753 [29] train-error:0.058323 [30] train-error:0.055893 [31] train-error:0.054678 [32] train-error:0.052248 [33] train-error:0.051033 [34] train-error:0.049818 [35] train-error:0.051033 [36] train-error:0.052248 [37] train-error:0.052248 [38] train-error:0.052248 [39] train-error:0.051033 [40] train-error:0.049818 [41] train-error:0.048603 [42] train-error:0.043742 [43] train-error:0.042527 [44] train-error:0.042527 [45] train-error:0.040097 [46] train-error:0.038882 [47] train-error:0.038882 [48] train-error:0.038882 [49] train-error:0.036452 [50] train-error:0.034022 [51] train-error:0.034022 [52] train-error:0.031592 [53] train-error:0.031592 [54] train-error:0.030377 [55] train-error:0.026731 [56] train-error:0.026731 [57] train-error:0.021871 [58] train-error:0.021871 [59] train-error:0.021871 [60] train-error:0.020656 [61] train-error:0.019441 [62] train-error:0.018226 [63] train-error:0.019441 [64] train-error:0.019441 [65] train-error:0.018226 [66] train-error:0.017011 [67] train-error:0.017011 [68] train-error:0.017011 [69] train-error:0.014581 [70] train-error:0.015796 [71] train-error:0.014581 [72] train-error:0.015796 [73] train-error:0.015796 [74] train-error:0.014581 [75] train-error:0.014581 [76] train-error:0.013366 [77] train-error:0.013366 [78] train-error:0.013366 [79] train-error:0.013366 [80] train-error:0.013366 [81] train-error:0.012151 [82] train-error:0.012151 [83] train-error:0.013366 [84] train-error:0.012151 [85] train-error:0.013366 [86] train-error:0.009721 [87] train-error:0.009721 [88] train-error:0.009721 [89] train-error:0.009721 [90] train-error:0.009721 [91] train-error:0.009721 [92] train-error:0.009721 [93] train-error:0.009721 [94] train-error:0.008505 [95] train-error:0.007290 [96] train-error:0.004860 [97] train-error:0.006075 [98] train-error:0.006075 [99] train-error:0.007290 [100] train-error:0.007290 [1] train-error:0.119077 [2] train-error:0.111786 [3] train-error:0.102066 [4] train-error:0.094775 [5] train-error:0.093560 [6] train-error:0.091130 [7] train-error:0.089915 [8] train-error:0.089915 [9] train-error:0.088700 [10] train-error:0.091130 [11] train-error:0.085055 [12] train-error:0.085055 [13] train-error:0.082625 [14] train-error:0.083840 [15] train-error:0.080194 [16] train-error:0.077764 [17] train-error:0.077764 [18] train-error:0.080194 [19] train-error:0.077764 [20] train-error:0.075334 [21] train-error:0.072904 [22] train-error:0.068044 [23] train-error:0.070474 [24] train-error:0.068044 [25] train-error:0.063183 [26] train-error:0.061968 [27] train-error:0.061968 [28] train-error:0.063183 [29] train-error:0.059538 [30] train-error:0.059538 [31] train-error:0.057108 [32] train-error:0.054678 [33] train-error:0.054678 [34] train-error:0.055893 [35] train-error:0.053463 [36] train-error:0.053463 [37] train-error:0.055893 [38] train-error:0.049818 [39] train-error:0.047388 [40] train-error:0.048603 [41] train-error:0.042527 [42] train-error:0.040097 [43] train-error:0.040097 [44] train-error:0.034022 [45] train-error:0.035237 [46] train-error:0.035237 [47] train-error:0.034022 [48] train-error:0.030377 [49] train-error:0.030377 [50] train-error:0.029162 [51] train-error:0.027947 [52] train-error:0.029162 [53] train-error:0.027947 [54] train-error:0.027947 [55] train-error:0.027947 [56] train-error:0.026731 [57] train-error:0.026731 [58] train-error:0.026731 [59] train-error:0.024301 [60] train-error:0.023086 [61] train-error:0.021871 [62] train-error:0.021871 [63] train-error:0.021871 [64] train-error:0.020656 [65] train-error:0.019441 [66] train-error:0.018226 [67] train-error:0.019441 [68] train-error:0.019441 [69] train-error:0.019441 [70] train-error:0.018226 [71] train-error:0.017011 [72] train-error:0.017011 [73] train-error:0.017011 [74] train-error:0.017011 [75] train-error:0.017011 [76] train-error:0.017011 [77] train-error:0.015796 [78] train-error:0.014581 [79] train-error:0.013366 [80] train-error:0.012151 [81] train-error:0.013366 [82] train-error:0.013366 [83] train-error:0.012151 [84] train-error:0.012151 [85] train-error:0.012151 [86] train-error:0.010936 [87] train-error:0.012151 [88] train-error:0.010936 [89] train-error:0.009721 [90] train-error:0.009721 [91] train-error:0.010936 [92] train-error:0.009721 [93] train-error:0.009721 [94] train-error:0.009721 [95] train-error:0.008505 [96] train-error:0.009721 [97] train-error:0.007290 [98] train-error:0.006075 [99] train-error:0.007290 [100] train-error:0.007290 [1] train-error:0.120438 [2] train-error:0.116788 [3] train-error:0.116788 [4] train-error:0.111922 [5] train-error:0.110706 [6] train-error:0.110706 [7] train-error:0.114355 [8] train-error:0.108273 [9] train-error:0.103406 [10] train-error:0.103406 [11] train-error:0.096107 [12] train-error:0.100973 [13] train-error:0.093674 [14] train-error:0.088808 [15] train-error:0.088808 [16] train-error:0.086375 [17] train-error:0.085158 [18] train-error:0.081509 [19] train-error:0.077859 [20] train-error:0.077859 [21] train-error:0.076642 [22] train-error:0.075426 [23] train-error:0.072993 [24] train-error:0.072993 [25] train-error:0.072993 [26] train-error:0.072993 [27] train-error:0.071776 [28] train-error:0.071776 [29] train-error:0.068127 [30] train-error:0.066910 [31] train-error:0.065693 [32] train-error:0.063260 [33] train-error:0.060827 [34] train-error:0.058394 [35] train-error:0.057178 [36] train-error:0.054745 [37] train-error:0.053528 [38] train-error:0.053528 [39] train-error:0.053528 [40] train-error:0.055961 [41] train-error:0.052311 [42] train-error:0.052311 [43] train-error:0.051095 [44] train-error:0.047445 [45] train-error:0.046229 [46] train-error:0.042579 [47] train-error:0.042579 [48] train-error:0.042579 [49] train-error:0.038929 [50] train-error:0.035280 [51] train-error:0.036496 [52] train-error:0.031630 [53] train-error:0.030414 [54] train-error:0.032847 [55] train-error:0.029197 [56] train-error:0.024331 [57] train-error:0.025547 [58] train-error:0.025547 [59] train-error:0.026764 [60] train-error:0.024331 [61] train-error:0.021898 [62] train-error:0.021898 [63] train-error:0.021898 [64] train-error:0.020681 [65] train-error:0.019465 [66] train-error:0.019465 [67] train-error:0.019465 [68] train-error:0.019465 [69] train-error:0.019465 [70] train-error:0.019465 [71] train-error:0.019465 [72] train-error:0.018248 [73] train-error:0.018248 [74] train-error:0.017032 [75] train-error:0.017032 [76] train-error:0.015815 [77] train-error:0.014599 [78] train-error:0.013382 [79] train-error:0.012165 [80] train-error:0.012165 [81] train-error:0.010949 [82] train-error:0.010949 [83] train-error:0.010949 [84] train-error:0.010949 [85] train-error:0.010949 [86] train-error:0.009732 [87] train-error:0.009732 [88] train-error:0.010949 [89] train-error:0.009732 [90] train-error:0.008516 [91] train-error:0.008516 [92] train-error:0.008516 [93] train-error:0.008516 [94] train-error:0.008516 [95] train-error:0.007299 [96] train-error:0.007299 [97] train-error:0.006083 [98] train-error:0.007299 [99] train-error:0.007299 [100] train-error:0.006083
[Tune-y] 7: acc.test.mean=0.8725503; time: 0.0 min [Tune-x] 8: max_depth=7; min_child_weight=3.89; subsample=0.823; colsample_bytree=0.835
[1] train-error:0.135036 [2] train-error:0.125304 [3] train-error:0.120438 [4] train-error:0.118005 [5] train-error:0.111922 [6] train-error:0.116788 [7] train-error:0.110706 [8] train-error:0.114355 [9] train-error:0.114355 [10] train-error:0.114355 [11] train-error:0.108273 [12] train-error:0.105839 [13] train-error:0.104623 [14] train-error:0.107056 [15] train-error:0.105839 [16] train-error:0.102190 [17] train-error:0.102190 [18] train-error:0.102190 [19] train-error:0.100973 [20] train-error:0.102190 [21] train-error:0.097324 [22] train-error:0.093674 [23] train-error:0.094891 [24] train-error:0.096107 [25] train-error:0.092457 [26] train-error:0.088808 [27] train-error:0.086375 [28] train-error:0.086375 [29] train-error:0.091241 [30] train-error:0.085158 [31] train-error:0.086375 [32] train-error:0.082725 [33] train-error:0.082725 [34] train-error:0.083942 [35] train-error:0.081509 [36] train-error:0.081509 [37] train-error:0.081509 [38] train-error:0.075426 [39] train-error:0.076642 [40] train-error:0.070560 [41] train-error:0.072993 [42] train-error:0.070560 [43] train-error:0.069343 [44] train-error:0.068127 [45] train-error:0.070560 [46] train-error:0.065693 [47] train-error:0.065693 [48] train-error:0.059611 [49] train-error:0.060827 [50] train-error:0.059611 [51] train-error:0.059611 [52] train-error:0.058394 [53] train-error:0.054745 [54] train-error:0.052311 [55] train-error:0.052311 [56] train-error:0.048662 [57] train-error:0.051095 [58] train-error:0.048662 [59] train-error:0.048662 [60] train-error:0.047445 [61] train-error:0.046229 [62] train-error:0.045012 [63] train-error:0.041363 [64] train-error:0.038929 [65] train-error:0.040146 [66] train-error:0.037713 [67] train-error:0.036496 [68] train-error:0.037713 [69] train-error:0.036496 [70] train-error:0.036496 [71] train-error:0.036496 [72] train-error:0.035280 [73] train-error:0.035280 [74] train-error:0.034063 [75] train-error:0.034063 [76] train-error:0.031630 [77] train-error:0.032847 [78] train-error:0.032847 [79] train-error:0.031630 [80] train-error:0.030414 [81] train-error:0.030414 [82] train-error:0.030414 [83] train-error:0.030414 [84] train-error:0.030414 [85] train-error:0.029197 [86] train-error:0.027981 [87] train-error:0.027981 [88] train-error:0.027981 [89] train-error:0.027981 [90] train-error:0.027981 [91] train-error:0.027981 [92] train-error:0.027981 [93] train-error:0.027981 [94] train-error:0.026764 [95] train-error:0.026764 [96] train-error:0.026764 [97] train-error:0.025547 [98] train-error:0.025547 [99] train-error:0.025547 [100] train-error:0.025547 [1] train-error:0.124088 [2] train-error:0.110706 [3] train-error:0.110706 [4] train-error:0.109489 [5] train-error:0.113139 [6] train-error:0.107056 [7] train-error:0.108273 [8] train-error:0.109489 [9] train-error:0.107056 [10] train-error:0.105839 [11] train-error:0.099757 [12] train-error:0.103406 [13] train-error:0.099757 [14] train-error:0.097324 [15] train-error:0.098540 [16] train-error:0.099757 [17] train-error:0.096107 [18] train-error:0.097324 [19] train-error:0.094891 [20] train-error:0.097324 [21] train-error:0.097324 [22] train-error:0.094891 [23] train-error:0.094891 [24] train-error:0.090024 [25] train-error:0.092457 [26] train-error:0.091241 [27] train-error:0.087591 [28] train-error:0.086375 [29] train-error:0.080292 [30] train-error:0.079075 [31] train-error:0.080292 [32] train-error:0.075426 [33] train-error:0.076642 [34] train-error:0.077859 [35] train-error:0.075426 [36] train-error:0.074209 [37] train-error:0.071776 [38] train-error:0.070560 [39] train-error:0.069343 [40] train-error:0.071776 [41] train-error:0.069343 [42] train-error:0.069343 [43] train-error:0.069343 [44] train-error:0.069343 [45] train-error:0.070560 [46] train-error:0.066910 [47] train-error:0.068127 [48] train-error:0.063260 [49] train-error:0.063260 [50] train-error:0.063260 [51] train-error:0.062044 [52] train-error:0.060827 [53] train-error:0.062044 [54] 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train-error:0.061968 [46] train-error:0.064399 [47] train-error:0.059538 [48] train-error:0.059538 [49] train-error:0.054678 [50] train-error:0.053463 [51] train-error:0.053463 [52] train-error:0.052248 [53] train-error:0.047388 [54] train-error:0.047388 [55] train-error:0.044957 [56] train-error:0.044957 [57] train-error:0.044957 [58] train-error:0.047388 [59] train-error:0.044957 [60] train-error:0.043742 [61] train-error:0.043742 [62] train-error:0.043742 [63] train-error:0.043742 [64] train-error:0.042527 [65] train-error:0.042527 [66] train-error:0.040097 [67] train-error:0.038882 [68] train-error:0.040097 [69] train-error:0.038882 [70] train-error:0.037667 [71] train-error:0.036452 [72] train-error:0.036452 [73] train-error:0.036452 [74] train-error:0.037667 [75] train-error:0.036452 [76] train-error:0.036452 [77] train-error:0.036452 [78] train-error:0.035237 [79] train-error:0.032807 [80] train-error:0.031592 [81] train-error:0.032807 [82] train-error:0.032807 [83] train-error:0.032807 [84] train-error:0.031592 [85] train-error:0.032807 [86] train-error:0.031592 [87] train-error:0.030377 [88] train-error:0.030377 [89] train-error:0.030377 [90] train-error:0.029162 [91] train-error:0.030377 [92] train-error:0.030377 [93] train-error:0.029162 [94] train-error:0.027947 [95] train-error:0.027947 [96] train-error:0.027947 [97] train-error:0.025516 [98] train-error:0.025516 [99] train-error:0.021871 [100] train-error:0.021871 [1] train-error:0.131387 [2] train-error:0.124088 [3] train-error:0.120438 [4] train-error:0.118005 [5] train-error:0.119221 [6] train-error:0.120438 [7] train-error:0.118005 [8] train-error:0.120438 [9] train-error:0.119221 [10] train-error:0.113139 [11] train-error:0.114355 [12] train-error:0.109489 [13] train-error:0.107056 [14] train-error:0.109489 [15] train-error:0.108273 [16] train-error:0.108273 [17] train-error:0.107056 [18] train-error:0.105839 [19] train-error:0.103406 [20] train-error:0.103406 [21] train-error:0.099757 [22] train-error:0.097324 [23] train-error:0.093674 [24] train-error:0.098540 [25] train-error:0.097324 [26] train-error:0.094891 [27] train-error:0.094891 [28] train-error:0.091241 [29] train-error:0.091241 [30] train-error:0.090024 [31] train-error:0.085158 [32] train-error:0.083942 [33] train-error:0.082725 [34] train-error:0.083942 [35] train-error:0.080292 [36] train-error:0.076642 [37] train-error:0.076642 [38] train-error:0.075426 [39] train-error:0.075426 [40] train-error:0.072993 [41] train-error:0.072993 [42] train-error:0.071776 [43] train-error:0.068127 [44] train-error:0.066910 [45] train-error:0.065693 [46] train-error:0.064477 [47] train-error:0.064477 [48] train-error:0.063260 [49] train-error:0.062044 [50] train-error:0.062044 [51] train-error:0.062044 [52] train-error:0.060827 [53] train-error:0.058394 [54] train-error:0.057178 [55] train-error:0.058394 [56] train-error:0.057178 [57] train-error:0.055961 [58] train-error:0.054745 [59] train-error:0.053528 [60] train-error:0.052311 [61] train-error:0.053528 [62] train-error:0.052311 [63] train-error:0.051095 [64] train-error:0.048662 [65] train-error:0.048662 [66] train-error:0.047445 [67] train-error:0.047445 [68] train-error:0.047445 [69] train-error:0.046229 [70] train-error:0.045012 [71] train-error:0.046229 [72] train-error:0.045012 [73] train-error:0.045012 [74] train-error:0.043796 [75] train-error:0.042579 [76] train-error:0.041363 [77] train-error:0.042579 [78] train-error:0.037713 [79] train-error:0.040146 [80] train-error:0.037713 [81] train-error:0.036496 [82] train-error:0.035280 [83] train-error:0.032847 [84] train-error:0.036496 [85] train-error:0.034063 [86] train-error:0.034063 [87] train-error:0.032847 [88] train-error:0.031630 [89] train-error:0.032847 [90] train-error:0.031630 [91] train-error:0.030414 [92] train-error:0.030414 [93] train-error:0.029197 [94] train-error:0.029197 [95] train-error:0.029197 [96] train-error:0.026764 [97] train-error:0.026764 [98] train-error:0.025547 [99] train-error:0.026764 [100] train-error:0.025547
[Tune-y] 8: acc.test.mean=0.8754724; time: 0.0 min [Tune-x] 9: max_depth=6; min_child_weight=3.07; subsample=0.702; colsample_bytree=0.911
[1] train-error:0.135036 [2] train-error:0.115572 [3] train-error:0.122871 [4] train-error:0.116788 [5] train-error:0.114355 [6] train-error:0.118005 [7] train-error:0.119221 [8] train-error:0.109489 [9] train-error:0.102190 [10] train-error:0.104623 [11] train-error:0.104623 [12] train-error:0.104623 [13] train-error:0.099757 [14] train-error:0.097324 [15] train-error:0.094891 [16] train-error:0.094891 [17] train-error:0.093674 [18] train-error:0.093674 [19] train-error:0.090024 [20] train-error:0.091241 [21] train-error:0.092457 [22] train-error:0.088808 [23] train-error:0.086375 [24] train-error:0.086375 [25] train-error:0.087591 [26] train-error:0.083942 [27] train-error:0.083942 [28] train-error:0.082725 [29] train-error:0.080292 [30] train-error:0.081509 [31] train-error:0.077859 [32] train-error:0.077859 [33] train-error:0.076642 [34] train-error:0.071776 [35] train-error:0.074209 [36] train-error:0.074209 [37] train-error:0.072993 [38] train-error:0.069343 [39] train-error:0.068127 [40] train-error:0.068127 [41] train-error:0.065693 [42] train-error:0.063260 [43] train-error:0.060827 [44] train-error:0.059611 [45] train-error:0.057178 [46] train-error:0.053528 [47] train-error:0.052311 [48] train-error:0.054745 [49] train-error:0.052311 [50] train-error:0.048662 [51] train-error:0.047445 [52] train-error:0.046229 [53] train-error:0.043796 [54] train-error:0.042579 [55] train-error:0.045012 [56] train-error:0.046229 [57] train-error:0.045012 [58] train-error:0.042579 [59] train-error:0.045012 [60] train-error:0.043796 [61] train-error:0.042579 [62] train-error:0.041363 [63] train-error:0.040146 [64] train-error:0.040146 [65] train-error:0.040146 [66] train-error:0.037713 [67] train-error:0.036496 [68] train-error:0.036496 [69] train-error:0.037713 [70] train-error:0.037713 [71] train-error:0.036496 [72] train-error:0.034063 [73] train-error:0.034063 [74] train-error:0.034063 [75] train-error:0.030414 [76] train-error:0.031630 [77] train-error:0.029197 [78] train-error:0.029197 [79] train-error:0.030414 [80] train-error:0.027981 [81] train-error:0.027981 [82] train-error:0.027981 [83] train-error:0.026764 [84] train-error:0.026764 [85] train-error:0.026764 [86] train-error:0.025547 [87] train-error:0.025547 [88] train-error:0.024331 [89] train-error:0.024331 [90] train-error:0.024331 [91] train-error:0.024331 [92] train-error:0.024331 [93] train-error:0.024331 [94] train-error:0.024331 [95] train-error:0.024331 [96] train-error:0.023114 [97] train-error:0.023114 [98] train-error:0.021898 [99] train-error:0.020681 [100] train-error:0.021898 [1] train-error:0.127737 [2] train-error:0.121655 [3] train-error:0.120438 [4] train-error:0.122871 [5] train-error:0.116788 [6] train-error:0.115572 [7] train-error:0.116788 [8] train-error:0.111922 [9] train-error:0.109489 [10] train-error:0.107056 [11] train-error:0.105839 [12] train-error:0.109489 [13] train-error:0.105839 [14] train-error:0.108273 [15] train-error:0.105839 [16] train-error:0.103406 [17] train-error:0.105839 [18] train-error:0.103406 [19] train-error:0.099757 [20] train-error:0.097324 [21] train-error:0.096107 [22] train-error:0.088808 [23] train-error:0.091241 [24] train-error:0.092457 [25] train-error:0.091241 [26] train-error:0.090024 [27] train-error:0.086375 [28] train-error:0.090024 [29] train-error:0.086375 [30] train-error:0.086375 [31] train-error:0.086375 [32] train-error:0.082725 [33] train-error:0.080292 [34] train-error:0.080292 [35] train-error:0.081509 [36] train-error:0.076642 [37] train-error:0.076642 [38] train-error:0.075426 [39] train-error:0.076642 [40] train-error:0.074209 [41] train-error:0.070560 [42] train-error:0.068127 [43] train-error:0.068127 [44] train-error:0.065693 [45] train-error:0.064477 [46] train-error:0.063260 [47] train-error:0.062044 [48] train-error:0.060827 [49] train-error:0.060827 [50] train-error:0.059611 [51] train-error:0.058394 [52] train-error:0.057178 [53] train-error:0.057178 [54] train-error:0.057178 [55] train-error:0.055961 [56] train-error:0.057178 [57] train-error:0.054745 [58] train-error:0.053528 [59] train-error:0.051095 [60] train-error:0.052311 [61] train-error:0.051095 [62] train-error:0.049878 [63] train-error:0.049878 [64] train-error:0.049878 [65] train-error:0.049878 [66] train-error:0.047445 [67] train-error:0.047445 [68] train-error:0.047445 [69] train-error:0.045012 [70] train-error:0.045012 [71] train-error:0.045012 [72] train-error:0.045012 [73] train-error:0.043796 [74] train-error:0.043796 [75] train-error:0.043796 [76] train-error:0.041363 [77] train-error:0.040146 [78] train-error:0.038929 [79] train-error:0.040146 [80] train-error:0.037713 [81] train-error:0.037713 [82] train-error:0.037713 [83] train-error:0.036496 [84] train-error:0.036496 [85] train-error:0.035280 [86] train-error:0.034063 [87] train-error:0.034063 [88] train-error:0.032847 [89] train-error:0.032847 [90] train-error:0.032847 [91] train-error:0.029197 [92] 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[31] train-error:0.076549 [32] train-error:0.076549 [33] train-error:0.076549 [34] train-error:0.075334 [35] train-error:0.074119 [36] train-error:0.071689 [37] train-error:0.072904 [38] train-error:0.072904 [39] train-error:0.072904 [40] train-error:0.071689 [41] train-error:0.070474 [42] train-error:0.069259 [43] train-error:0.068044 [44] train-error:0.066829 [45] train-error:0.063183 [46] train-error:0.061968 [47] train-error:0.061968 [48] train-error:0.058323 [49] train-error:0.060753 [50] train-error:0.060753 [51] train-error:0.060753 [52] train-error:0.059538 [53] train-error:0.055893 [54] train-error:0.057108 [55] train-error:0.052248 [56] train-error:0.051033 [57] train-error:0.051033 [58] train-error:0.047388 [59] train-error:0.046173 [60] train-error:0.048603 [61] train-error:0.046173 [62] train-error:0.043742 [63] train-error:0.041312 [64] train-error:0.040097 [65] train-error:0.041312 [66] train-error:0.042527 [67] train-error:0.040097 [68] train-error:0.041312 [69] 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[22] train-error:0.098540 [23] train-error:0.090024 [24] train-error:0.082725 [25] train-error:0.081509 [26] train-error:0.082725 [27] train-error:0.083942 [28] train-error:0.081509 [29] train-error:0.081509 [30] train-error:0.076642 [31] train-error:0.080292 [32] train-error:0.077859 [33] train-error:0.077859 [34] train-error:0.074209 [35] train-error:0.069343 [36] train-error:0.068127 [37] train-error:0.068127 [38] train-error:0.065693 [39] train-error:0.068127 [40] train-error:0.064477 [41] train-error:0.063260 [42] train-error:0.059611 [43] train-error:0.059611 [44] train-error:0.059611 [45] train-error:0.057178 [46] train-error:0.058394 [47] train-error:0.058394 [48] train-error:0.057178 [49] train-error:0.057178 [50] train-error:0.052311 [51] train-error:0.052311 [52] train-error:0.052311 [53] train-error:0.049878 [54] train-error:0.046229 [55] train-error:0.048662 [56] train-error:0.043796 [57] train-error:0.043796 [58] train-error:0.041363 [59] train-error:0.042579 [60] train-error:0.038929 [61] train-error:0.041363 [62] train-error:0.040146 [63] train-error:0.036496 [64] train-error:0.037713 [65] train-error:0.036496 [66] train-error:0.034063 [67] train-error:0.036496 [68] train-error:0.035280 [69] train-error:0.035280 [70] train-error:0.035280 [71] train-error:0.034063 [72] train-error:0.032847 [73] train-error:0.031630 [74] train-error:0.032847 [75] train-error:0.031630 [76] train-error:0.029197 [77] train-error:0.030414 [78] train-error:0.026764 [79] train-error:0.027981 [80] train-error:0.027981 [81] train-error:0.027981 [82] train-error:0.029197 [83] train-error:0.027981 [84] train-error:0.029197 [85] train-error:0.027981 [86] train-error:0.029197 [87] train-error:0.029197 [88] train-error:0.027981 [89] train-error:0.026764 [90] train-error:0.024331 [91] train-error:0.021898 [92] train-error:0.024331 [93] train-error:0.024331 [94] train-error:0.021898 [95] train-error:0.021898 [96] train-error:0.021898 [97] train-error:0.021898 [98] train-error:0.020681 [99] train-error:0.021898 [100] train-error:0.021898
[Tune-y] 9: acc.test.mean=0.8686621; time: 0.0 min [Tune-x] 10: max_depth=4; min_child_weight=7.54; subsample=0.913; colsample_bytree=0.586
[1] train-error:0.144769 [2] train-error:0.149635 [3] train-error:0.148418 [4] train-error:0.143552 [5] train-error:0.143552 [6] train-error:0.139903 [7] train-error:0.141119 [8] train-error:0.142336 [9] train-error:0.136253 [10] train-error:0.135036 [11] train-error:0.138686 [12] train-error:0.133820 [13] train-error:0.130170 [14] train-error:0.133820 [15] train-error:0.130170 [16] train-error:0.130170 [17] train-error:0.125304 [18] train-error:0.126521 [19] train-error:0.127737 [20] train-error:0.125304 [21] train-error:0.124088 [22] train-error:0.125304 [23] train-error:0.116788 [24] train-error:0.116788 [25] train-error:0.115572 [26] train-error:0.115572 [27] train-error:0.115572 [28] train-error:0.111922 [29] train-error:0.110706 [30] train-error:0.108273 [31] train-error:0.104623 [32] train-error:0.108273 [33] train-error:0.107056 [34] train-error:0.109489 [35] train-error:0.107056 [36] train-error:0.108273 [37] train-error:0.103406 [38] train-error:0.103406 [39] train-error:0.100973 [40] train-error:0.100973 [41] train-error:0.099757 [42] train-error:0.099757 [43] train-error:0.098540 [44] train-error:0.098540 [45] train-error:0.098540 [46] train-error:0.098540 [47] train-error:0.097324 [48] train-error:0.098540 [49] train-error:0.096107 [50] train-error:0.094891 [51] train-error:0.096107 [52] train-error:0.093674 [53] train-error:0.093674 [54] train-error:0.096107 [55] train-error:0.092457 [56] train-error:0.093674 [57] train-error:0.091241 [58] train-error:0.092457 [59] train-error:0.087591 [60] train-error:0.088808 [61] train-error:0.087591 [62] train-error:0.086375 [63] train-error:0.087591 [64] train-error:0.087591 [65] train-error:0.085158 [66] train-error:0.083942 [67] train-error:0.083942 [68] train-error:0.082725 [69] train-error:0.081509 [70] train-error:0.080292 [71] train-error:0.081509 [72] train-error:0.080292 [73] train-error:0.079075 [74] train-error:0.080292 [75] train-error:0.081509 [76] train-error:0.080292 [77] 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[16] train-error:0.116788 [17] train-error:0.110706 [18] train-error:0.111922 [19] train-error:0.111922 [20] train-error:0.110706 [21] train-error:0.110706 [22] train-error:0.109489 [23] train-error:0.110706 [24] train-error:0.110706 [25] train-error:0.110706 [26] train-error:0.107056 [27] train-error:0.107056 [28] train-error:0.107056 [29] train-error:0.108273 [30] train-error:0.105839 [31] train-error:0.104623 [32] train-error:0.105839 [33] train-error:0.102190 [34] train-error:0.104623 [35] train-error:0.097324 [36] train-error:0.099757 [37] train-error:0.097324 [38] train-error:0.097324 [39] train-error:0.092457 [40] train-error:0.093674 [41] train-error:0.092457 [42] train-error:0.092457 [43] train-error:0.092457 [44] train-error:0.093674 [45] train-error:0.092457 [46] train-error:0.093674 [47] train-error:0.092457 [48] train-error:0.090024 [49] train-error:0.088808 [50] train-error:0.090024 [51] train-error:0.088808 [52] train-error:0.085158 [53] train-error:0.086375 [54] train-error:0.083942 [55] train-error:0.085158 [56] train-error:0.086375 [57] train-error:0.083942 [58] train-error:0.083942 [59] train-error:0.083942 [60] train-error:0.082725 [61] train-error:0.080292 [62] train-error:0.081509 [63] train-error:0.081509 [64] train-error:0.082725 [65] train-error:0.081509 [66] train-error:0.080292 [67] train-error:0.081509 [68] train-error:0.081509 [69] train-error:0.080292 [70] train-error:0.079075 [71] train-error:0.079075 [72] train-error:0.077859 [73] train-error:0.079075 [74] train-error:0.079075 [75] train-error:0.077859 [76] train-error:0.077859 [77] train-error:0.077859 [78] train-error:0.077859 [79] train-error:0.077859 [80] train-error:0.076642 [81] train-error:0.076642 [82] train-error:0.075426 [83] train-error:0.075426 [84] train-error:0.071776 [85] train-error:0.071776 [86] train-error:0.071776 [87] train-error:0.071776 [88] train-error:0.070560 [89] train-error:0.071776 [90] train-error:0.071776 [91] train-error:0.071776 [92] 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[31] train-error:0.110571 [32] train-error:0.109356 [33] train-error:0.109356 [34] train-error:0.109356 [35] train-error:0.109356 [36] train-error:0.109356 [37] train-error:0.108141 [38] train-error:0.106926 [39] train-error:0.106926 [40] train-error:0.106926 [41] train-error:0.106926 [42] train-error:0.105711 [43] train-error:0.102066 [44] train-error:0.102066 [45] train-error:0.099635 [46] train-error:0.098420 [47] train-error:0.099635 [48] train-error:0.097205 [49] train-error:0.100851 [50] train-error:0.095990 [51] train-error:0.094775 [52] train-error:0.091130 [53] train-error:0.089915 [54] train-error:0.092345 [55] train-error:0.089915 [56] train-error:0.092345 [57] train-error:0.091130 [58] train-error:0.089915 [59] train-error:0.088700 [60] train-error:0.088700 [61] train-error:0.086270 [62] train-error:0.086270 [63] train-error:0.086270 [64] train-error:0.086270 [65] train-error:0.086270 [66] train-error:0.085055 [67] train-error:0.083840 [68] train-error:0.083840 [69] 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train-error:0.137303 [8] train-error:0.133657 [9] train-error:0.134872 [10] train-error:0.128797 [11] train-error:0.128797 [12] train-error:0.127582 [13] train-error:0.127582 [14] train-error:0.130012 [15] train-error:0.126367 [16] train-error:0.125152 [17] train-error:0.126367 [18] train-error:0.122722 [19] train-error:0.125152 [20] train-error:0.117861 [21] train-error:0.117861 [22] train-error:0.117861 [23] train-error:0.114216 [24] train-error:0.111786 [25] train-error:0.111786 [26] train-error:0.111786 [27] train-error:0.111786 [28] train-error:0.111786 [29] train-error:0.106926 [30] train-error:0.110571 [31] train-error:0.102066 [32] train-error:0.104496 [33] train-error:0.099635 [34] train-error:0.099635 [35] train-error:0.099635 [36] train-error:0.098420 [37] train-error:0.098420 [38] train-error:0.098420 [39] train-error:0.095990 [40] train-error:0.095990 [41] train-error:0.094775 [42] train-error:0.094775 [43] train-error:0.092345 [44] train-error:0.088700 [45] train-error:0.088700 [46] train-error:0.088700 [47] train-error:0.088700 [48] train-error:0.087485 [49] train-error:0.089915 [50] train-error:0.088700 [51] train-error:0.087485 [52] train-error:0.087485 [53] train-error:0.085055 [54] train-error:0.087485 [55] train-error:0.086270 [56] train-error:0.085055 [57] train-error:0.082625 [58] train-error:0.080194 [59] train-error:0.082625 [60] train-error:0.078979 [61] train-error:0.078979 [62] train-error:0.078979 [63] train-error:0.078979 [64] train-error:0.077764 [65] train-error:0.076549 [66] train-error:0.072904 [67] train-error:0.075334 [68] train-error:0.075334 [69] train-error:0.071689 [70] train-error:0.071689 [71] train-error:0.071689 [72] train-error:0.072904 [73] train-error:0.071689 [74] train-error:0.072904 [75] train-error:0.072904 [76] train-error:0.070474 [77] train-error:0.070474 [78] train-error:0.069259 [79] train-error:0.070474 [80] train-error:0.071689 [81] train-error:0.070474 [82] train-error:0.070474 [83] train-error:0.069259 [84] train-error:0.071689 [85] train-error:0.070474 [86] train-error:0.069259 [87] train-error:0.068044 [88] train-error:0.066829 [89] train-error:0.066829 [90] train-error:0.065614 [91] train-error:0.065614 [92] train-error:0.065614 [93] train-error:0.063183 [94] train-error:0.064399 [95] train-error:0.063183 [96] train-error:0.063183 [97] train-error:0.060753 [98] train-error:0.061968 [99] train-error:0.060753 [100] train-error:0.064399 [1] train-error:0.139903 [2] train-error:0.137470 [3] train-error:0.137470 [4] train-error:0.137470 [5] train-error:0.133820 [6] train-error:0.136253 [7] train-error:0.132603 [8] train-error:0.133820 [9] train-error:0.133820 [10] train-error:0.133820 [11] train-error:0.131387 [12] train-error:0.130170 [13] train-error:0.127737 [14] train-error:0.126521 [15] train-error:0.128954 [16] train-error:0.126521 [17] train-error:0.130170 [18] train-error:0.128954 [19] train-error:0.130170 [20] train-error:0.125304 [21] train-error:0.125304 [22] train-error:0.120438 [23] train-error:0.120438 [24] train-error:0.120438 [25] train-error:0.118005 [26] train-error:0.119221 [27] train-error:0.119221 [28] train-error:0.116788 [29] train-error:0.116788 [30] train-error:0.116788 [31] train-error:0.116788 [32] train-error:0.116788 [33] train-error:0.113139 [34] train-error:0.111922 [35] train-error:0.107056 [36] train-error:0.107056 [37] train-error:0.107056 [38] train-error:0.105839 [39] train-error:0.105839 [40] train-error:0.105839 [41] train-error:0.104623 [42] train-error:0.102190 [43] train-error:0.104623 [44] train-error:0.102190 [45] train-error:0.100973 [46] train-error:0.100973 [47] train-error:0.098540 [48] train-error:0.098540 [49] train-error:0.096107 [50] train-error:0.094891 [51] train-error:0.096107 [52] train-error:0.094891 [53] train-error:0.091241 [54] train-error:0.090024 [55] train-error:0.091241 [56] train-error:0.088808 [57] train-error:0.088808 [58] train-error:0.090024 [59] train-error:0.087591 [60] train-error:0.086375 [61] train-error:0.083942 [62] train-error:0.083942 [63] train-error:0.083942 [64] train-error:0.081509 [65] train-error:0.081509 [66] train-error:0.081509 [67] train-error:0.081509 [68] train-error:0.080292 [69] train-error:0.080292 [70] train-error:0.080292 [71] train-error:0.079075 [72] train-error:0.080292 [73] train-error:0.080292 [74] train-error:0.080292 [75] train-error:0.079075 [76] train-error:0.079075 [77] train-error:0.079075 [78] train-error:0.080292 [79] train-error:0.081509 [80] train-error:0.080292 [81] train-error:0.079075 [82] train-error:0.077859 [83] train-error:0.076642 [84] train-error:0.077859 [85] train-error:0.076642 [86] train-error:0.077859 [87] train-error:0.075426 [88] train-error:0.075426 [89] train-error:0.075426 [90] train-error:0.074209 [91] train-error:0.075426 [92] train-error:0.075426 [93] train-error:0.074209 [94] train-error:0.075426 [95] train-error:0.072993 [96] train-error:0.072993 [97] train-error:0.072993 [98] train-error:0.072993 [99] train-error:0.071776 [100] train-error:0.071776
[Tune-y] 10: acc.test.mean=0.8715794; time: 0.0 min [Tune] Result: max_depth=6; min_child_weight=4.51; subsample=0.522; colsample_bytree=0.669 : acc.test.mean=0.8754914
setDT(tuned.train) #set train set to data table
setDT(tuned.test) #set test set to data table
tuned.train_label <- tuned.train$Attrition # assign target label for train set to a new field tuned.train_label
tuned.test_label <- tuned.test$Attrition # assign target label for test set to a new field tuned.test_label
tuned.new_train <- model.matrix(~.+0,data = tuned.train[,-c("Attrition"),with=F]) # convert tuned.train to matrix format and assign to new variable tuned.new_train
tuned.new_test <- model.matrix(~.+0,data = tuned.test[,-c("Attrition"),with=F]) # convert tuned.test to matrix format and assign to new variable tuned.new_test
tuned.train_label <- as.numeric(tuned.train_label)-1 #convert tuned.train_label to numeric - 1 so we have (0,1) instead of(1,2)
tuned.test_label <- as.numeric(tuned.test_label)-1 #convert tuned.test_label to numeric - 1 so we have (0,1) instead of(1,2)
tuned.dtrain <- xgb.DMatrix(data = tuned.new_train,label = tuned.train_label ) #Construct xgb.DMatrix object from train set and assign to tuned.dtrain
tuned.dtest <- xgb.DMatrix(data = tuned.new_test,label = tuned.test_label) #Construct xgb.DMatrix object from test set and assign to tuned.dtest
#assign parameters based on best paramaters from hypertune/cross validation
params <- list(booster = 'gbtree', objective = "binary:logistic",
eta=0.3, gamma=0, max_depth= mytune$x$max_depth,
min_child_weight=mytune$x$min_child_weight, subsample=mytune$x$subsample,
colsample_bytree= mytune$x$colsample_bytree)
#Train hypertuned xgb model using assigned params previously defined
tuned.xgb <- xgb.train (params = params, data = tuned.dtrain, nrounds = 79,
watchlist = list(val=tuned.dtest,train=tuned.dtrain), print_every_n = 10,
early_stopping_rounds = 10, maximize = F , eval_metric = "error")
[1] val-error:0.176471 train-error:0.143969 Multiple eval metrics are present. Will use train_error for early stopping. Will train until train_error hasn't improved in 10 rounds. [11] val-error:0.174208 train-error:0.113813 [21] val-error:0.149321 train-error:0.091440 [31] val-error:0.144796 train-error:0.076848 [41] val-error:0.142534 train-error:0.060311 [51] val-error:0.135747 train-error:0.054475 [61] val-error:0.140271 train-error:0.042802 [71] val-error:0.128959 train-error:0.042802 Stopping. Best iteration: [63] val-error:0.140271 train-error:0.041829
tuned.xgbpred <- predict (tuned.xgb,tuned.dtest) #predict model using test set
tuned.xgbpred <- ifelse(tuned.xgbpred > 0.5,1,0) #if probablity > 0.5 set prediction to class 1 else set prediction to class 0
#Feature importance plot for base xgboost model
tuned.mat <- xgb.importance (feature_names = colnames(tuned.new_train),model = tuned.xgb)
xgb.plot.importance (importance_matrix = tuned.mat[1:10] , rel_to_first = TRUE, xlab = "Relative importance Hypertuned XGBOOST" )
tuned.test$Attrition <- as.numeric(tuned.test$Attrition)-1 #convert target label to numeric - 1 so we have (0,1) instead of(1,2)
options(repr.plot.width=8, repr.plot.height=6)
#store results table in conf_df
conf_df <- data.frame(table(tuned.test$Attrition, tuned.xgbpred))
#plotting of confusion matrix using ggplot2 library
ggplot(data = conf_df, mapping = aes(x = tuned.xgbpred, y = Var1)) +
geom_tile(aes(fill = Freq), colour = "white") +
geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1) +
scale_fill_gradient(low = "#F3F781", high = "#58FA82") + theme(legend.position="none", strip.background = element_blank(), strip.text.x = element_blank(),
plot.title=element_text(hjust=0.5, color="white"), plot.subtitle=element_text(colour="white"), plot.background=element_rect(fill="#0D7680"),
axis.text.x=element_text(colour="white"), axis.text.y=element_text(colour="white"),
axis.title=element_text(colour="white"),
legend.background = element_rect(fill="#FFF9F5",
size=0.5, linetype="solid",
colour ="black")) +
labs(title="Confusion Matrix Hypertuned XGBOOST", y="Actual Attrrition", x="Predicted Attrition")
#xgb.plot.tree(model=tuned.xgb, tree=0)
#xgb.plot.tree(model=tuned.xgb, tree=1)
#store table of predicted and actual values
tuned.cmxgboost<- table(tuned.test$Attrition, tuned.xgbpred)
#apply accuracy formula on table
xgboost.hypertunedaccuracy <- model.accuracy(tuned.cmxgboost)
#apply recall formula on yes cases of attrtiion
xgboost.hypertunedrecallYes <- AttritionYes.recall(tuned.cmxgboost)
#apply precision formula on yes cases of attrtiion
xgboost.hypertunedprecisionYes <- AttritionYes.precision(tuned.cmxgboost)
#calculate F1 Score for yes cases of attrtiion
xgboost.hypertuned_F1ScoreYes <- round((2 * xgboost.hypertunedprecisionYes * xgboost.hypertunedrecallYes) / (xgboost.hypertunedrecallYes
+xgboost.hypertunedprecisionYes) ,digits=2)
xgboost.hypertunedrecallNo <- AttritionNo.recall(tuned.cmxgboost) #apply recall formula on no cases of attrtiion
xgboost.hypertunedprecisionNo <- AttritionNo.precision(tuned.cmxgboost) #apply precision formula on no cases of attrtiion
#calculate F1 Score for no cases of attrtiion
xgboost.hypertuned_F1ScoreNo <- round((2 * xgboost.hypertunedprecisionNo * xgboost.hypertunedrecallNo) / (xgboost.hypertunedrecallNo
+xgboost.hypertunedprecisionNo) ,digits=2)
total.recall.hypertuned_XGBoost<- total.recall(tuned.cmxgboost) #apply total recall formula (both cases of attrtiion)
total.precision.hypertunedXGBoost <- total.precision(tuned.cmxgboost) #apply total precision formula (both cases of attrtiion)
total.F1Score.hypertunedXGBoost<- round((xgboost.hypertuned_F1ScoreYes + xgboost.hypertuned_F1ScoreNo)/2,digits=2) #calculate total f1 score (both cases of attrtiion)
smote.tuned.test <- test # assign new variable smote.tuned.test as training set from test
smote.tuned.train <- train_smote # assign new variable smote.tuned.train as training set from train_smote
setDF(smote.tuned.train) # Set smote.tuned.train to a data frame
smote.fact_col <- colnames(smote.tuned.train)[sapply(smote.tuned.train,is.character)] # check for charactar columns and store them in smote.fact_col variable
for(i in smote.fact_col) set(smote.tuned.train,j=i,value = factor(smote.tuned.train[[i]])) # convert training data to factor
for (i in smote.fact_col) set(smote.tuned.test,j=i,value = factor(smote.tuned.test[[i]])) # convert testing data to factor
#create tasks
smote.tuned.traintask <- makeClassifTask (data = smote.tuned.train,target = "Attrition") #create classification task for train set
smote.tuned.testtask <- makeClassifTask (data = smote.tuned.test,target = "Attrition") #create classification task for test set
#one hot encoding for train adn test classification tasks
smote.tuned.traintask <- createDummyFeatures (obj = smote.tuned.traintask )
smote.tuned.testtask <- createDummyFeatures (obj = smote.tuned.testtask )
#create learner to be used for model training
lrn <- makeLearner("classif.xgboost",predict.type = "response")
#Specify parameter values for the learner previously defined
lrn$par.vals <- list( objective="binary:logistic", eval_metric="error", nrounds=100L, eta=0.1)
#Set parameters to be used for hyperparameter tuning
params <- makeParamSet(makeIntegerParam("max_depth",lower = 3L,upper = 10L), makeNumericParam("min_child_weight",lower = 1L,upper = 10L), makeNumericParam("subsample",lower = 0.5,upper = 1), makeNumericParam("colsample_bytree",lower = 0.5,upper = 1))
rdesc <- makeResampleDesc("CV",stratify = T,iters=5L)#set resampling strategy to 5 fold cross validation . Set stratify = True
ctrl <- makeTuneControlRandom(maxit = 10L) #Set number of iterations for random search to a maximum of 10
#train xgboost boost model
smotetuneXG <- tuneParams(learner = lrn, task = smote.tuned.traintask, resampling = rdesc, measures = acc, par.set = params, control = ctrl, show.info = T)
[Tune] Started tuning learner classif.xgboost for parameter set:
Type len Def Constr Req Tunable Trafo
max_depth integer - - 3 to 10 - TRUE -
min_child_weight numeric - - 1 to 10 - TRUE -
subsample numeric - - 0.5 to 1 - TRUE -
colsample_bytree numeric - - 0.5 to 1 - TRUE -
With control class: TuneControlRandom
Imputation value: -0
[Tune-x] 1: max_depth=7; min_child_weight=1.35; subsample=0.855; colsample_bytree=0.673
[1] train-error:0.146970 [2] train-error:0.104545 [3] train-error:0.087879 [4] train-error:0.084848 [5] train-error:0.072727 [6] train-error:0.066667 [7] train-error:0.062121 [8] train-error:0.050000 [9] train-error:0.045455 [10] train-error:0.040909 [11] train-error:0.042424 [12] train-error:0.034848 [13] train-error:0.036364 [14] train-error:0.034848 [15] train-error:0.034848 [16] train-error:0.030303 [17] train-error:0.028788 [18] train-error:0.028788 [19] train-error:0.024242 [20] train-error:0.021212 [21] train-error:0.019697 [22] train-error:0.019697 [23] train-error:0.019697 [24] train-error:0.018182 [25] train-error:0.016667 [26] train-error:0.015152 [27] train-error:0.013636 [28] train-error:0.012121 [29] train-error:0.012121 [30] train-error:0.007576 [31] train-error:0.012121 [32] train-error:0.007576 [33] train-error:0.006061 [34] train-error:0.004545 [35] train-error:0.004545 [36] train-error:0.001515 [37] train-error:0.003030 [38] train-error:0.001515 [39] train-error:0.001515 [40] train-error:0.001515 [41] train-error:0.001515 [42] train-error:0.001515 [43] train-error:0.001515 [44] train-error:0.001515 [45] train-error:0.001515 [46] train-error:0.001515 [47] train-error:0.001515 [48] train-error:0.000000 [49] train-error:0.000000 [50] train-error:0.000000 [51] train-error:0.001515 [52] train-error:0.000000 [53] train-error:0.000000 [54] train-error:0.000000 [55] train-error:0.000000 [56] train-error:0.000000 [57] train-error:0.000000 [58] train-error:0.000000 [59] train-error:0.000000 [60] train-error:0.000000 [61] train-error:0.000000 [62] train-error:0.000000 [63] train-error:0.000000 [64] train-error:0.000000 [65] train-error:0.000000 [66] train-error:0.000000 [67] train-error:0.000000 [68] train-error:0.000000 [69] train-error:0.000000 [70] train-error:0.000000 [71] train-error:0.000000 [72] train-error:0.000000 [73] train-error:0.000000 [74] train-error:0.000000 [75] train-error:0.000000 [76] train-error:0.000000 [77] train-error:0.000000 [78] train-error:0.000000 [79] train-error:0.000000 [80] train-error:0.000000 [81] train-error:0.000000 [82] train-error:0.000000 [83] train-error:0.000000 [84] train-error:0.000000 [85] train-error:0.000000 [86] train-error:0.000000 [87] train-error:0.000000 [88] train-error:0.000000 [89] train-error:0.000000 [90] train-error:0.000000 [91] train-error:0.000000 [92] train-error:0.000000 [93] train-error:0.000000 [94] train-error:0.000000 [95] train-error:0.000000 [96] train-error:0.000000 [97] train-error:0.000000 [98] train-error:0.000000 [99] train-error:0.000000 [100] train-error:0.000000 [1] train-error:0.175758 [2] train-error:0.118182 [3] train-error:0.103030 [4] train-error:0.071212 [5] train-error:0.056061 [6] train-error:0.059091 [7] train-error:0.057576 [8] train-error:0.053030 [9] train-error:0.043939 [10] train-error:0.034848 [11] train-error:0.036364 [12] train-error:0.030303 [13] train-error:0.027273 [14] train-error:0.024242 [15] train-error:0.022727 [16] train-error:0.019697 [17] train-error:0.018182 [18] train-error:0.015152 [19] train-error:0.013636 [20] train-error:0.013636 [21] train-error:0.007576 [22] train-error:0.009091 [23] train-error:0.006061 [24] train-error:0.006061 [25] train-error:0.007576 [26] train-error:0.004545 [27] train-error:0.004545 [28] train-error:0.003030 [29] train-error:0.003030 [30] train-error:0.003030 [31] train-error:0.003030 [32] train-error:0.003030 [33] train-error:0.003030 [34] train-error:0.003030 [35] train-error:0.003030 [36] train-error:0.001515 [37] train-error:0.001515 [38] train-error:0.001515 [39] train-error:0.001515 [40] train-error:0.001515 [41] train-error:0.001515 [42] train-error:0.001515 [43] train-error:0.001515 [44] train-error:0.001515 [45] train-error:0.001515 [46] train-error:0.001515 [47] train-error:0.001515 [48] train-error:0.001515 [49] train-error:0.001515 [50] train-error:0.001515 [51] train-error:0.001515 [52] train-error:0.001515 [53] train-error:0.000000 [54] train-error:0.000000 [55] train-error:0.000000 [56] train-error:0.000000 [57] train-error:0.000000 [58] train-error:0.000000 [59] train-error:0.000000 [60] train-error:0.000000 [61] train-error:0.000000 [62] train-error:0.000000 [63] train-error:0.000000 [64] train-error:0.000000 [65] train-error:0.000000 [66] train-error:0.000000 [67] train-error:0.000000 [68] train-error:0.000000 [69] train-error:0.000000 [70] train-error:0.000000 [71] train-error:0.000000 [72] train-error:0.000000 [73] train-error:0.000000 [74] train-error:0.000000 [75] train-error:0.000000 [76] train-error:0.000000 [77] train-error:0.000000 [78] train-error:0.000000 [79] train-error:0.000000 [80] train-error:0.000000 [81] train-error:0.000000 [82] train-error:0.000000 [83] train-error:0.000000 [84] train-error:0.000000 [85] train-error:0.000000 [86] train-error:0.000000 [87] train-error:0.000000 [88] train-error:0.000000 [89] train-error:0.000000 [90] train-error:0.000000 [91] train-error:0.000000 [92] 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[Tune-y] 1: acc.test.mean=0.8909091; time: 0.0 min [Tune-x] 2: max_depth=8; min_child_weight=6.3; subsample=0.904; colsample_bytree=0.891
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[22] train-error:0.075758 [23] train-error:0.071212 [24] train-error:0.075758 [25] train-error:0.071212 [26] train-error:0.072727 [27] train-error:0.068182 [28] train-error:0.065152 [29] train-error:0.066667 [30] train-error:0.066667 [31] train-error:0.066667 [32] train-error:0.057576 [33] train-error:0.054545 [34] train-error:0.054545 [35] train-error:0.050000 [36] train-error:0.054545 [37] train-error:0.054545 [38] train-error:0.048485 [39] train-error:0.048485 [40] train-error:0.050000 [41] train-error:0.046970 [42] train-error:0.046970 [43] train-error:0.042424 [44] train-error:0.042424 [45] train-error:0.040909 [46] train-error:0.040909 [47] train-error:0.037879 [48] train-error:0.037879 [49] train-error:0.037879 [50] train-error:0.037879 [51] train-error:0.036364 [52] train-error:0.036364 [53] train-error:0.037879 [54] train-error:0.037879 [55] train-error:0.036364 [56] train-error:0.034848 [57] train-error:0.036364 [58] train-error:0.031818 [59] train-error:0.031818 [60] train-error:0.033333 [61] train-error:0.031818 [62] train-error:0.030303 [63] train-error:0.028788 [64] train-error:0.028788 [65] train-error:0.028788 [66] train-error:0.030303 [67] train-error:0.031818 [68] train-error:0.027273 [69] train-error:0.031818 [70] train-error:0.030303 [71] train-error:0.027273 [72] train-error:0.024242 [73] train-error:0.024242 [74] train-error:0.024242 [75] train-error:0.024242 [76] train-error:0.021212 [77] train-error:0.021212 [78] train-error:0.022727 [79] train-error:0.021212 [80] train-error:0.021212 [81] train-error:0.019697 [82] train-error:0.019697 [83] train-error:0.022727 [84] train-error:0.021212 [85] train-error:0.019697 [86] train-error:0.019697 [87] train-error:0.018182 [88] train-error:0.016667 [89] train-error:0.018182 [90] train-error:0.018182 [91] train-error:0.016667 [92] train-error:0.016667 [93] train-error:0.013636 [94] train-error:0.013636 [95] train-error:0.013636 [96] train-error:0.015152 [97] train-error:0.013636 [98] train-error:0.013636 [99] train-error:0.012121 [100] train-error:0.012121
[Tune-y] 2: acc.test.mean=0.8751515; time: 0.0 min [Tune-x] 3: max_depth=9; min_child_weight=6.85; subsample=0.993; colsample_bytree=0.96
[1] train-error:0.222727 [2] train-error:0.203030 [3] train-error:0.187879 [4] train-error:0.187879 [5] train-error:0.174242 [6] train-error:0.165152 [7] train-error:0.154545 [8] train-error:0.153030 [9] train-error:0.151515 [10] train-error:0.140909 [11] train-error:0.131818 [12] train-error:0.119697 [13] train-error:0.121212 [14] train-error:0.118182 [15] train-error:0.116667 [16] train-error:0.106061 [17] train-error:0.100000 [18] train-error:0.100000 [19] train-error:0.084848 [20] train-error:0.092424 [21] train-error:0.087879 [22] train-error:0.081818 [23] train-error:0.080303 [24] train-error:0.077273 [25] train-error:0.074242 [26] train-error:0.074242 [27] train-error:0.069697 [28] train-error:0.069697 [29] train-error:0.072727 [30] train-error:0.057576 [31] train-error:0.053030 [32] train-error:0.054545 [33] train-error:0.048485 [34] train-error:0.050000 [35] train-error:0.050000 [36] train-error:0.046970 [37] train-error:0.045455 [38] train-error:0.045455 [39] train-error:0.043939 [40] train-error:0.042424 [41] train-error:0.042424 [42] train-error:0.037879 [43] train-error:0.037879 [44] train-error:0.036364 [45] train-error:0.037879 [46] train-error:0.034848 [47] train-error:0.033333 [48] train-error:0.031818 [49] train-error:0.031818 [50] train-error:0.027273 [51] train-error:0.028788 [52] train-error:0.027273 [53] train-error:0.030303 [54] train-error:0.034848 [55] train-error:0.031818 [56] train-error:0.030303 [57] train-error:0.028788 [58] train-error:0.030303 [59] train-error:0.027273 [60] train-error:0.025758 [61] train-error:0.022727 [62] train-error:0.021212 [63] train-error:0.021212 [64] train-error:0.021212 [65] train-error:0.019697 [66] train-error:0.018182 [67] train-error:0.019697 [68] train-error:0.019697 [69] train-error:0.016667 [70] train-error:0.016667 [71] train-error:0.018182 [72] train-error:0.016667 [73] train-error:0.018182 [74] train-error:0.016667 [75] train-error:0.013636 [76] train-error:0.013636 [77] train-error:0.016667 [78] train-error:0.015152 [79] train-error:0.013636 [80] train-error:0.013636 [81] train-error:0.018182 [82] train-error:0.015152 [83] train-error:0.013636 [84] train-error:0.012121 [85] train-error:0.012121 [86] train-error:0.013636 [87] train-error:0.013636 [88] train-error:0.013636 [89] train-error:0.015152 [90] train-error:0.013636 [91] train-error:0.013636 [92] train-error:0.013636 [93] train-error:0.013636 [94] train-error:0.013636 [95] train-error:0.013636 [96] train-error:0.013636 [97] train-error:0.013636 [98] train-error:0.013636 [99] train-error:0.013636 [100] train-error:0.013636 [1] train-error:0.233333 [2] train-error:0.222727 [3] train-error:0.162121 [4] train-error:0.166667 [5] train-error:0.174242 [6] train-error:0.166667 [7] train-error:0.160606 [8] train-error:0.157576 [9] train-error:0.145455 [10] train-error:0.134848 [11] train-error:0.119697 [12] train-error:0.119697 [13] train-error:0.119697 [14] train-error:0.103030 [15] train-error:0.101515 [16] train-error:0.090909 [17] train-error:0.096970 [18] train-error:0.090909 [19] train-error:0.084848 [20] train-error:0.089394 [21] train-error:0.080303 [22] train-error:0.080303 [23] train-error:0.075758 [24] train-error:0.071212 [25] train-error:0.065152 [26] train-error:0.062121 [27] train-error:0.059091 [28] train-error:0.062121 [29] train-error:0.059091 [30] train-error:0.057576 [31] train-error:0.054545 [32] train-error:0.056061 [33] train-error:0.054545 [34] train-error:0.048485 [35] train-error:0.051515 [36] train-error:0.046970 [37] train-error:0.050000 [38] train-error:0.043939 [39] train-error:0.040909 [40] train-error:0.043939 [41] train-error:0.039394 [42] train-error:0.040909 [43] train-error:0.039394 [44] train-error:0.036364 [45] train-error:0.030303 [46] train-error:0.034848 [47] train-error:0.036364 [48] train-error:0.034848 [49] train-error:0.033333 [50] train-error:0.031818 [51] train-error:0.031818 [52] train-error:0.028788 [53] train-error:0.025758 [54] train-error:0.027273 [55] train-error:0.028788 [56] train-error:0.028788 [57] train-error:0.028788 [58] train-error:0.027273 [59] train-error:0.025758 [60] train-error:0.024242 [61] train-error:0.027273 [62] train-error:0.027273 [63] train-error:0.024242 [64] train-error:0.027273 [65] train-error:0.025758 [66] train-error:0.024242 [67] train-error:0.021212 [68] train-error:0.019697 [69] train-error:0.021212 [70] train-error:0.021212 [71] train-error:0.021212 [72] train-error:0.018182 [73] train-error:0.021212 [74] train-error:0.022727 [75] train-error:0.019697 [76] train-error:0.019697 [77] train-error:0.018182 [78] train-error:0.016667 [79] train-error:0.018182 [80] train-error:0.016667 [81] train-error:0.018182 [82] train-error:0.015152 [83] train-error:0.015152 [84] train-error:0.013636 [85] train-error:0.015152 [86] train-error:0.013636 [87] train-error:0.013636 [88] train-error:0.015152 [89] train-error:0.013636 [90] train-error:0.013636 [91] train-error:0.013636 [92] train-error:0.013636 [93] train-error:0.013636 [94] train-error:0.013636 [95] train-error:0.013636 [96] train-error:0.013636 [97] train-error:0.013636 [98] train-error:0.012121 [99] train-error:0.012121 [100] train-error:0.013636 [1] train-error:0.243939 [2] train-error:0.198485 [3] train-error:0.187879 [4] train-error:0.181818 [5] train-error:0.162121 [6] train-error:0.160606 [7] train-error:0.142424 [8] train-error:0.140909 [9] train-error:0.136364 [10] train-error:0.130303 [11] train-error:0.122727 [12] train-error:0.118182 [13] train-error:0.119697 [14] train-error:0.109091 [15] train-error:0.104545 [16] train-error:0.103030 [17] train-error:0.092424 [18] train-error:0.087879 [19] train-error:0.077273 [20] train-error:0.071212 [21] train-error:0.069697 [22] train-error:0.062121 [23] train-error:0.068182 [24] train-error:0.063636 [25] train-error:0.065152 [26] train-error:0.057576 [27] train-error:0.057576 [28] train-error:0.057576 [29] train-error:0.053030 [30] train-error:0.053030 [31] train-error:0.051515 [32] train-error:0.051515 [33] train-error:0.048485 [34] train-error:0.048485 [35] train-error:0.050000 [36] train-error:0.048485 [37] train-error:0.048485 [38] train-error:0.046970 [39] train-error:0.043939 [40] train-error:0.043939 [41] train-error:0.042424 [42] train-error:0.042424 [43] train-error:0.042424 [44] train-error:0.040909 [45] train-error:0.033333 [46] train-error:0.031818 [47] train-error:0.031818 [48] train-error:0.031818 [49] train-error:0.033333 [50] train-error:0.030303 [51] train-error:0.030303 [52] train-error:0.030303 [53] train-error:0.030303 [54] train-error:0.031818 [55] train-error:0.028788 [56] train-error:0.027273 [57] train-error:0.027273 [58] train-error:0.030303 [59] train-error:0.027273 [60] train-error:0.025758 [61] train-error:0.028788 [62] train-error:0.027273 [63] train-error:0.028788 [64] train-error:0.025758 [65] train-error:0.024242 [66] train-error:0.022727 [67] train-error:0.022727 [68] train-error:0.022727 [69] train-error:0.022727 [70] train-error:0.022727 [71] train-error:0.021212 [72] train-error:0.019697 [73] train-error:0.019697 [74] train-error:0.019697 [75] train-error:0.018182 [76] train-error:0.018182 [77] train-error:0.015152 [78] train-error:0.015152 [79] train-error:0.015152 [80] train-error:0.015152 [81] train-error:0.015152 [82] train-error:0.015152 [83] train-error:0.015152 [84] train-error:0.015152 [85] train-error:0.015152 [86] train-error:0.015152 [87] train-error:0.015152 [88] train-error:0.018182 [89] train-error:0.016667 [90] train-error:0.013636 [91] train-error:0.016667 [92] train-error:0.013636 [93] train-error:0.013636 [94] train-error:0.013636 [95] train-error:0.013636 [96] train-error:0.013636 [97] train-error:0.013636 [98] train-error:0.015152 [99] train-error:0.013636 [100] train-error:0.013636 [1] train-error:0.227273 [2] train-error:0.196970 [3] train-error:0.184848 [4] train-error:0.181818 [5] train-error:0.181818 [6] train-error:0.175758 [7] train-error:0.162121 [8] train-error:0.156061 [9] train-error:0.136364 [10] train-error:0.130303 [11] train-error:0.134848 [12] train-error:0.133333 [13] train-error:0.127273 [14] train-error:0.118182 [15] train-error:0.110606 [16] train-error:0.107576 [17] train-error:0.101515 [18] train-error:0.101515 [19] train-error:0.096970 [20] train-error:0.090909 [21] train-error:0.098485 [22] train-error:0.093939 [23] train-error:0.090909 [24] train-error:0.081818 [25] train-error:0.080303 [26] train-error:0.078788 [27] train-error:0.074242 [28] train-error:0.069697 [29] train-error:0.071212 [30] train-error:0.071212 [31] train-error:0.065152 [32] train-error:0.062121 [33] train-error:0.057576 [34] train-error:0.056061 [35] train-error:0.053030 [36] train-error:0.050000 [37] train-error:0.056061 [38] train-error:0.053030 [39] train-error:0.050000 [40] train-error:0.048485 [41] train-error:0.045455 [42] train-error:0.042424 [43] train-error:0.042424 [44] train-error:0.040909 [45] train-error:0.042424 [46] train-error:0.039394 [47] train-error:0.040909 [48] train-error:0.037879 [49] train-error:0.037879 [50] train-error:0.040909 [51] train-error:0.039394 [52] train-error:0.037879 [53] train-error:0.034848 [54] train-error:0.034848 [55] train-error:0.028788 [56] train-error:0.030303 [57] train-error:0.031818 [58] train-error:0.030303 [59] train-error:0.028788 [60] train-error:0.027273 [61] train-error:0.027273 [62] train-error:0.024242 [63] train-error:0.021212 [64] train-error:0.021212 [65] train-error:0.021212 [66] train-error:0.021212 [67] train-error:0.021212 [68] train-error:0.024242 [69] train-error:0.021212 [70] train-error:0.019697 [71] train-error:0.018182 [72] train-error:0.018182 [73] train-error:0.018182 [74] train-error:0.018182 [75] train-error:0.016667 [76] train-error:0.016667 [77] train-error:0.016667 [78] train-error:0.016667 [79] train-error:0.016667 [80] train-error:0.015152 [81] train-error:0.015152 [82] train-error:0.016667 [83] train-error:0.016667 [84] train-error:0.016667 [85] train-error:0.013636 [86] train-error:0.012121 [87] train-error:0.012121 [88] train-error:0.013636 [89] train-error:0.012121 [90] train-error:0.012121 [91] train-error:0.012121 [92] train-error:0.012121 [93] train-error:0.009091 [94] train-error:0.007576 [95] train-error:0.012121 [96] train-error:0.009091 [97] train-error:0.009091 [98] train-error:0.009091 [99] train-error:0.007576 [100] train-error:0.007576 [1] train-error:0.221212 [2] train-error:0.218182 [3] train-error:0.209091 [4] train-error:0.181818 [5] train-error:0.159091 [6] train-error:0.160606 [7] train-error:0.156061 [8] train-error:0.143939 [9] train-error:0.142424 [10] train-error:0.124242 [11] train-error:0.119697 [12] train-error:0.115152 [13] train-error:0.110606 [14] train-error:0.107576 [15] train-error:0.109091 [16] train-error:0.098485 [17] train-error:0.095455 [18] train-error:0.096970 [19] train-error:0.089394 [20] train-error:0.087879 [21] train-error:0.080303 [22] train-error:0.072727 [23] train-error:0.071212 [24] train-error:0.072727 [25] train-error:0.071212 [26] train-error:0.065152 [27] train-error:0.065152 [28] train-error:0.060606 [29] train-error:0.059091 [30] train-error:0.056061 [31] train-error:0.057576 [32] train-error:0.054545 [33] train-error:0.053030 [34] train-error:0.051515 [35] train-error:0.050000 [36] train-error:0.046970 [37] train-error:0.050000 [38] train-error:0.050000 [39] train-error:0.046970 [40] train-error:0.045455 [41] train-error:0.046970 [42] train-error:0.048485 [43] train-error:0.048485 [44] train-error:0.046970 [45] train-error:0.045455 [46] train-error:0.046970 [47] train-error:0.046970 [48] train-error:0.043939 [49] train-error:0.043939 [50] train-error:0.043939 [51] train-error:0.043939 [52] train-error:0.043939 [53] train-error:0.043939 [54] train-error:0.039394 [55] train-error:0.037879 [56] train-error:0.037879 [57] train-error:0.036364 [58] train-error:0.033333 [59] train-error:0.033333 [60] train-error:0.034848 [61] train-error:0.031818 [62] train-error:0.031818 [63] train-error:0.030303 [64] train-error:0.030303 [65] train-error:0.028788 [66] train-error:0.024242 [67] train-error:0.027273 [68] train-error:0.027273 [69] train-error:0.024242 [70] train-error:0.024242 [71] train-error:0.024242 [72] train-error:0.021212 [73] train-error:0.021212 [74] train-error:0.019697 [75] train-error:0.019697 [76] train-error:0.021212 [77] train-error:0.019697 [78] train-error:0.024242 [79] train-error:0.021212 [80] train-error:0.021212 [81] train-error:0.022727 [82] train-error:0.022727 [83] train-error:0.025758 [84] train-error:0.019697 [85] train-error:0.019697 [86] train-error:0.019697 [87] train-error:0.018182 [88] train-error:0.015152 [89] train-error:0.019697 [90] train-error:0.018182 [91] train-error:0.015152 [92] train-error:0.018182 [93] train-error:0.018182 [94] train-error:0.016667 [95] train-error:0.015152 [96] train-error:0.015152 [97] train-error:0.012121 [98] train-error:0.013636 [99] train-error:0.013636 [100] train-error:0.015152
[Tune-y] 3: acc.test.mean=0.8739394; time: 0.0 min [Tune-x] 4: max_depth=5; min_child_weight=5.08; subsample=0.661; colsample_bytree=0.576
[1] train-error:0.254545 [2] train-error:0.181818 [3] train-error:0.172727 [4] train-error:0.184848 [5] train-error:0.157576 [6] train-error:0.142424 [7] train-error:0.131818 [8] train-error:0.128788 [9] train-error:0.124242 [10] train-error:0.130303 [11] train-error:0.128788 [12] train-error:0.131818 [13] train-error:0.127273 [14] train-error:0.136364 [15] train-error:0.125758 [16] train-error:0.119697 [17] train-error:0.115152 [18] train-error:0.112121 [19] train-error:0.112121 [20] train-error:0.109091 [21] train-error:0.107576 [22] train-error:0.100000 [23] train-error:0.098485 [24] train-error:0.087879 [25] train-error:0.087879 [26] train-error:0.090909 [27] train-error:0.089394 [28] train-error:0.084848 [29] train-error:0.080303 [30] train-error:0.084848 [31] train-error:0.075758 [32] train-error:0.072727 [33] train-error:0.069697 [34] train-error:0.069697 [35] train-error:0.066667 [36] train-error:0.065152 [37] train-error:0.066667 [38] train-error:0.062121 [39] train-error:0.059091 [40] train-error:0.059091 [41] train-error:0.066667 [42] train-error:0.059091 [43] train-error:0.062121 [44] train-error:0.057576 [45] train-error:0.060606 [46] train-error:0.060606 [47] train-error:0.059091 [48] train-error:0.054545 [49] train-error:0.054545 [50] train-error:0.053030 [51] train-error:0.053030 [52] train-error:0.053030 [53] train-error:0.056061 [54] train-error:0.053030 [55] train-error:0.054545 [56] train-error:0.053030 [57] train-error:0.050000 [58] train-error:0.050000 [59] train-error:0.048485 [60] train-error:0.048485 [61] train-error:0.046970 [62] train-error:0.045455 [63] train-error:0.043939 [64] train-error:0.042424 [65] train-error:0.043939 [66] train-error:0.045455 [67] train-error:0.045455 [68] train-error:0.042424 [69] train-error:0.042424 [70] train-error:0.040909 [71] train-error:0.039394 [72] train-error:0.040909 [73] train-error:0.045455 [74] train-error:0.043939 [75] train-error:0.039394 [76] train-error:0.040909 [77] 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[16] train-error:0.122727 [17] train-error:0.124242 [18] train-error:0.116667 [19] train-error:0.115152 [20] train-error:0.116667 [21] train-error:0.110606 [22] train-error:0.110606 [23] train-error:0.109091 [24] train-error:0.104545 [25] train-error:0.098485 [26] train-error:0.096970 [27] train-error:0.089394 [28] train-error:0.083333 [29] train-error:0.087879 [30] train-error:0.090909 [31] train-error:0.092424 [32] train-error:0.084848 [33] train-error:0.086364 [34] train-error:0.081818 [35] train-error:0.081818 [36] train-error:0.078788 [37] train-error:0.078788 [38] train-error:0.075758 [39] train-error:0.074242 [40] train-error:0.072727 [41] train-error:0.071212 [42] train-error:0.069697 [43] train-error:0.066667 [44] train-error:0.063636 [45] train-error:0.066667 [46] train-error:0.065152 [47] train-error:0.060606 [48] train-error:0.063636 [49] train-error:0.060606 [50] train-error:0.054545 [51] train-error:0.048485 [52] train-error:0.051515 [53] train-error:0.050000 [54] 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[31] train-error:0.080303 [32] train-error:0.075758 [33] train-error:0.075758 [34] train-error:0.071212 [35] train-error:0.069697 [36] train-error:0.066667 [37] train-error:0.066667 [38] train-error:0.062121 [39] train-error:0.059091 [40] train-error:0.062121 [41] train-error:0.063636 [42] train-error:0.062121 [43] train-error:0.060606 [44] train-error:0.056061 [45] train-error:0.054545 [46] train-error:0.054545 [47] train-error:0.050000 [48] train-error:0.050000 [49] train-error:0.046970 [50] train-error:0.048485 [51] train-error:0.045455 [52] train-error:0.048485 [53] train-error:0.046970 [54] train-error:0.046970 [55] train-error:0.048485 [56] train-error:0.046970 [57] train-error:0.048485 [58] train-error:0.048485 [59] train-error:0.048485 [60] train-error:0.050000 [61] train-error:0.051515 [62] train-error:0.046970 [63] train-error:0.040909 [64] train-error:0.042424 [65] train-error:0.045455 [66] train-error:0.042424 [67] train-error:0.043939 [68] train-error:0.037879 [69] 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[22] train-error:0.092424 [23] train-error:0.093939 [24] train-error:0.090909 [25] train-error:0.090909 [26] train-error:0.087879 [27] train-error:0.080303 [28] train-error:0.078788 [29] train-error:0.072727 [30] train-error:0.077273 [31] train-error:0.077273 [32] train-error:0.068182 [33] train-error:0.068182 [34] train-error:0.068182 [35] train-error:0.066667 [36] train-error:0.068182 [37] train-error:0.066667 [38] train-error:0.065152 [39] train-error:0.068182 [40] train-error:0.063636 [41] train-error:0.062121 [42] train-error:0.057576 [43] train-error:0.059091 [44] train-error:0.057576 [45] train-error:0.054545 [46] train-error:0.057576 [47] train-error:0.062121 [48] train-error:0.059091 [49] train-error:0.057576 [50] train-error:0.051515 [51] train-error:0.053030 [52] train-error:0.056061 [53] train-error:0.053030 [54] train-error:0.056061 [55] train-error:0.053030 [56] train-error:0.045455 [57] train-error:0.045455 [58] train-error:0.046970 [59] train-error:0.045455 [60] train-error:0.045455 [61] train-error:0.043939 [62] train-error:0.042424 [63] train-error:0.045455 [64] train-error:0.046970 [65] train-error:0.046970 [66] train-error:0.050000 [67] train-error:0.046970 [68] train-error:0.043939 [69] train-error:0.043939 [70] train-error:0.043939 [71] train-error:0.042424 [72] train-error:0.039394 [73] train-error:0.042424 [74] train-error:0.043939 [75] train-error:0.043939 [76] train-error:0.042424 [77] train-error:0.040909 [78] train-error:0.040909 [79] train-error:0.040909 [80] train-error:0.040909 [81] train-error:0.039394 [82] train-error:0.040909 [83] train-error:0.040909 [84] train-error:0.033333 [85] train-error:0.036364 [86] train-error:0.033333 [87] train-error:0.036364 [88] train-error:0.034848 [89] train-error:0.033333 [90] train-error:0.031818 [91] train-error:0.031818 [92] train-error:0.028788 [93] train-error:0.030303 [94] train-error:0.028788 [95] train-error:0.028788 [96] train-error:0.030303 [97] train-error:0.028788 [98] train-error:0.025758 [99] train-error:0.027273 [100] train-error:0.027273
[Tune-y] 4: acc.test.mean=0.8727273; time: 0.0 min [Tune-x] 5: max_depth=9; min_child_weight=1.75; subsample=0.784; colsample_bytree=0.669
[1] train-error:0.175758 [2] train-error:0.140909 [3] train-error:0.112121 [4] train-error:0.109091 [5] train-error:0.081818 [6] train-error:0.068182 [7] train-error:0.071212 [8] train-error:0.059091 [9] train-error:0.053030 [10] train-error:0.046970 [11] train-error:0.039394 [12] train-error:0.040909 [13] train-error:0.039394 [14] train-error:0.033333 [15] train-error:0.030303 [16] train-error:0.027273 [17] train-error:0.019697 [18] train-error:0.021212 [19] train-error:0.021212 [20] train-error:0.013636 [21] train-error:0.013636 [22] train-error:0.012121 [23] train-error:0.013636 [24] train-error:0.010606 [25] train-error:0.010606 [26] train-error:0.012121 [27] train-error:0.009091 [28] train-error:0.010606 [29] train-error:0.006061 [30] train-error:0.006061 [31] train-error:0.004545 [32] train-error:0.006061 [33] train-error:0.004545 [34] train-error:0.004545 [35] train-error:0.004545 [36] train-error:0.004545 [37] train-error:0.004545 [38] train-error:0.003030 [39] 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[Tune-y] 5: acc.test.mean=0.8824242; time: 0.0 min [Tune-x] 6: max_depth=6; min_child_weight=9.78; subsample=0.726; colsample_bytree=0.681
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train-error:0.178788 [8] train-error:0.183333 [9] train-error:0.169697 [10] train-error:0.177273 [11] train-error:0.178788 [12] train-error:0.166667 [13] train-error:0.166667 [14] train-error:0.157576 [15] train-error:0.159091 [16] train-error:0.151515 [17] train-error:0.148485 [18] train-error:0.154545 [19] train-error:0.151515 [20] train-error:0.154545 [21] train-error:0.150000 [22] train-error:0.143939 [23] train-error:0.140909 [24] train-error:0.139394 [25] train-error:0.140909 [26] train-error:0.136364 [27] train-error:0.133333 [28] train-error:0.137879 [29] train-error:0.134848 [30] train-error:0.128788 [31] train-error:0.124242 [32] train-error:0.116667 [33] train-error:0.115152 [34] train-error:0.106061 [35] train-error:0.107576 [36] train-error:0.106061 [37] train-error:0.104545 [38] train-error:0.101515 [39] train-error:0.101515 [40] train-error:0.104545 [41] train-error:0.104545 [42] train-error:0.096970 [43] train-error:0.100000 [44] train-error:0.095455 [45] train-error:0.098485 [46] train-error:0.095455 [47] train-error:0.093939 [48] train-error:0.092424 [49] train-error:0.093939 [50] train-error:0.087879 [51] train-error:0.087879 [52] train-error:0.092424 [53] train-error:0.081818 [54] train-error:0.081818 [55] train-error:0.084848 [56] train-error:0.087879 [57] train-error:0.084848 [58] train-error:0.083333 [59] train-error:0.083333 [60] train-error:0.080303 [61] train-error:0.072727 [62] train-error:0.078788 [63] train-error:0.071212 [64] train-error:0.074242 [65] train-error:0.074242 [66] train-error:0.077273 [67] train-error:0.075758 [68] train-error:0.072727 [69] train-error:0.075758 [70] train-error:0.072727 [71] train-error:0.071212 [72] train-error:0.069697 [73] train-error:0.069697 [74] train-error:0.071212 [75] train-error:0.069697 [76] train-error:0.071212 [77] train-error:0.071212 [78] train-error:0.069697 [79] train-error:0.069697 [80] train-error:0.071212 [81] train-error:0.069697 [82] train-error:0.069697 [83] train-error:0.069697 [84] train-error:0.069697 [85] train-error:0.068182 [86] train-error:0.068182 [87] train-error:0.068182 [88] train-error:0.069697 [89] train-error:0.066667 [90] train-error:0.068182 [91] train-error:0.066667 [92] train-error:0.065152 [93] train-error:0.063636 [94] train-error:0.063636 [95] train-error:0.062121 [96] train-error:0.062121 [97] train-error:0.062121 [98] train-error:0.063636 [99] train-error:0.065152 [100] train-error:0.065152 [1] train-error:0.250000 [2] train-error:0.228788 [3] train-error:0.222727 [4] train-error:0.215152 [5] train-error:0.203030 [6] train-error:0.181818 [7] train-error:0.186364 [8] train-error:0.187879 [9] train-error:0.186364 [10] train-error:0.180303 [11] train-error:0.178788 [12] train-error:0.180303 [13] train-error:0.190909 [14] train-error:0.186364 [15] train-error:0.180303 [16] train-error:0.177273 [17] train-error:0.174242 [18] train-error:0.171212 [19] train-error:0.166667 [20] train-error:0.160606 [21] train-error:0.157576 [22] train-error:0.151515 [23] train-error:0.148485 [24] train-error:0.148485 [25] train-error:0.139394 [26] train-error:0.143939 [27] train-error:0.142424 [28] train-error:0.137879 [29] train-error:0.137879 [30] train-error:0.137879 [31] train-error:0.128788 [32] train-error:0.130303 [33] train-error:0.127273 [34] train-error:0.124242 [35] train-error:0.121212 [36] train-error:0.122727 [37] train-error:0.121212 [38] train-error:0.118182 [39] train-error:0.115152 [40] train-error:0.116667 [41] train-error:0.113636 [42] train-error:0.110606 [43] train-error:0.107576 [44] train-error:0.106061 [45] train-error:0.106061 [46] train-error:0.106061 [47] train-error:0.103030 [48] train-error:0.103030 [49] train-error:0.103030 [50] train-error:0.100000 [51] train-error:0.098485 [52] train-error:0.101515 [53] train-error:0.098485 [54] train-error:0.103030 [55] train-error:0.100000 [56] train-error:0.095455 [57] train-error:0.100000 [58] train-error:0.096970 [59] train-error:0.095455 [60] train-error:0.092424 [61] train-error:0.089394 [62] train-error:0.090909 [63] train-error:0.087879 [64] train-error:0.087879 [65] train-error:0.086364 [66] train-error:0.077273 [67] train-error:0.078788 [68] train-error:0.077273 [69] train-error:0.080303 [70] train-error:0.080303 [71] train-error:0.078788 [72] train-error:0.078788 [73] train-error:0.080303 [74] train-error:0.081818 [75] train-error:0.081818 [76] train-error:0.080303 [77] train-error:0.074242 [78] train-error:0.074242 [79] train-error:0.071212 [80] train-error:0.071212 [81] train-error:0.071212 [82] train-error:0.069697 [83] train-error:0.071212 [84] train-error:0.068182 [85] train-error:0.072727 [86] train-error:0.069697 [87] train-error:0.071212 [88] train-error:0.068182 [89] train-error:0.068182 [90] train-error:0.066667 [91] train-error:0.068182 [92] train-error:0.066667 [93] train-error:0.063636 [94] train-error:0.065152 [95] train-error:0.063636 [96] train-error:0.066667 [97] train-error:0.069697 [98] train-error:0.068182 [99] train-error:0.068182 [100] train-error:0.066667
[Tune-y] 6: acc.test.mean=0.8387879; time: 0.0 min [Tune-x] 7: max_depth=4; min_child_weight=6.81; subsample=0.561; colsample_bytree=0.935
[1] train-error:0.277273 [2] train-error:0.266667 [3] train-error:0.251515 [4] train-error:0.233333 [5] train-error:0.207576 [6] train-error:0.200000 [7] train-error:0.175758 [8] train-error:0.181818 [9] train-error:0.178788 [10] train-error:0.172727 [11] train-error:0.157576 [12] train-error:0.154545 [13] train-error:0.154545 [14] train-error:0.146970 [15] train-error:0.156061 [16] train-error:0.151515 [17] train-error:0.148485 [18] train-error:0.154545 [19] train-error:0.157576 [20] train-error:0.153030 [21] train-error:0.153030 [22] train-error:0.150000 [23] train-error:0.150000 [24] train-error:0.150000 [25] train-error:0.143939 [26] train-error:0.148485 [27] train-error:0.140909 [28] train-error:0.145455 [29] train-error:0.139394 [30] train-error:0.139394 [31] train-error:0.139394 [32] train-error:0.128788 [33] train-error:0.121212 [34] train-error:0.130303 [35] train-error:0.124242 [36] train-error:0.119697 [37] train-error:0.118182 [38] train-error:0.113636 [39] train-error:0.116667 [40] train-error:0.115152 [41] train-error:0.119697 [42] train-error:0.115152 [43] train-error:0.116667 [44] train-error:0.116667 [45] train-error:0.110606 [46] train-error:0.109091 [47] train-error:0.109091 [48] train-error:0.106061 [49] train-error:0.106061 [50] train-error:0.100000 [51] train-error:0.098485 [52] train-error:0.096970 [53] train-error:0.095455 [54] train-error:0.095455 [55] train-error:0.093939 [56] train-error:0.093939 [57] train-error:0.095455 [58] train-error:0.092424 [59] train-error:0.098485 [60] train-error:0.090909 [61] train-error:0.096970 [62] train-error:0.095455 [63] train-error:0.092424 [64] train-error:0.093939 [65] train-error:0.089394 [66] train-error:0.092424 [67] train-error:0.089394 [68] train-error:0.089394 [69] train-error:0.084848 [70] train-error:0.089394 [71] train-error:0.086364 [72] train-error:0.092424 [73] train-error:0.084848 [74] train-error:0.092424 [75] train-error:0.087879 [76] train-error:0.093939 [77] train-error:0.087879 [78] train-error:0.084848 [79] train-error:0.083333 [80] train-error:0.080303 [81] train-error:0.077273 [82] train-error:0.077273 [83] train-error:0.075758 [84] train-error:0.080303 [85] train-error:0.074242 [86] train-error:0.069697 [87] train-error:0.072727 [88] train-error:0.071212 [89] train-error:0.066667 [90] train-error:0.068182 [91] train-error:0.068182 [92] train-error:0.071212 [93] train-error:0.069697 [94] train-error:0.074242 [95] train-error:0.071212 [96] train-error:0.069697 [97] train-error:0.065152 [98] train-error:0.063636 [99] train-error:0.063636 [100] train-error:0.060606 [1] train-error:0.253030 [2] train-error:0.265152 [3] train-error:0.239394 [4] train-error:0.224242 [5] train-error:0.222727 [6] train-error:0.212121 [7] train-error:0.207576 [8] train-error:0.203030 [9] train-error:0.186364 [10] train-error:0.183333 [11] train-error:0.181818 [12] train-error:0.168182 [13] train-error:0.169697 [14] train-error:0.168182 [15] train-error:0.143939 [16] train-error:0.148485 [17] train-error:0.153030 [18] train-error:0.148485 [19] train-error:0.150000 [20] train-error:0.139394 [21] train-error:0.142424 [22] train-error:0.143939 [23] train-error:0.140909 [24] train-error:0.140909 [25] train-error:0.125758 [26] train-error:0.130303 [27] train-error:0.127273 [28] train-error:0.124242 [29] train-error:0.119697 [30] train-error:0.118182 [31] train-error:0.113636 [32] train-error:0.109091 [33] train-error:0.106061 [34] train-error:0.107576 [35] train-error:0.104545 [36] train-error:0.101515 [37] train-error:0.095455 [38] train-error:0.096970 [39] train-error:0.096970 [40] train-error:0.095455 [41] train-error:0.093939 [42] train-error:0.089394 [43] train-error:0.087879 [44] train-error:0.083333 [45] train-error:0.086364 [46] train-error:0.083333 [47] train-error:0.083333 [48] train-error:0.084848 [49] train-error:0.083333 [50] train-error:0.084848 [51] train-error:0.077273 [52] train-error:0.072727 [53] train-error:0.080303 [54] train-error:0.080303 [55] train-error:0.077273 [56] train-error:0.077273 [57] train-error:0.075758 [58] train-error:0.071212 [59] train-error:0.074242 [60] train-error:0.068182 [61] train-error:0.069697 [62] train-error:0.066667 [63] train-error:0.066667 [64] train-error:0.068182 [65] train-error:0.071212 [66] train-error:0.069697 [67] train-error:0.068182 [68] train-error:0.063636 [69] train-error:0.068182 [70] train-error:0.068182 [71] train-error:0.068182 [72] train-error:0.065152 [73] train-error:0.068182 [74] train-error:0.066667 [75] train-error:0.068182 [76] train-error:0.065152 [77] train-error:0.059091 [78] train-error:0.063636 [79] train-error:0.059091 [80] train-error:0.057576 [81] train-error:0.057576 [82] train-error:0.059091 [83] train-error:0.059091 [84] train-error:0.059091 [85] train-error:0.057576 [86] train-error:0.057576 [87] train-error:0.059091 [88] train-error:0.057576 [89] train-error:0.057576 [90] train-error:0.056061 [91] train-error:0.056061 [92] train-error:0.059091 [93] train-error:0.054545 [94] train-error:0.053030 [95] train-error:0.051515 [96] train-error:0.050000 [97] train-error:0.053030 [98] train-error:0.053030 [99] train-error:0.048485 [100] train-error:0.050000 [1] train-error:0.274242 [2] train-error:0.222727 [3] train-error:0.212121 [4] train-error:0.210606 [5] train-error:0.198485 [6] train-error:0.181818 [7] train-error:0.177273 [8] train-error:0.181818 [9] train-error:0.186364 [10] train-error:0.177273 [11] train-error:0.181818 [12] train-error:0.178788 [13] train-error:0.159091 [14] train-error:0.171212 [15] train-error:0.166667 [16] train-error:0.154545 [17] train-error:0.146970 [18] train-error:0.150000 [19] train-error:0.142424 [20] train-error:0.145455 [21] train-error:0.139394 [22] train-error:0.137879 [23] train-error:0.134848 [24] train-error:0.133333 [25] train-error:0.128788 [26] train-error:0.124242 [27] train-error:0.119697 [28] train-error:0.122727 [29] train-error:0.118182 [30] train-error:0.122727 [31] train-error:0.116667 [32] train-error:0.119697 [33] train-error:0.122727 [34] train-error:0.121212 [35] train-error:0.121212 [36] train-error:0.121212 [37] train-error:0.115152 [38] train-error:0.116667 [39] train-error:0.116667 [40] train-error:0.112121 [41] train-error:0.110606 [42] train-error:0.110606 [43] train-error:0.106061 [44] train-error:0.107576 [45] train-error:0.107576 [46] train-error:0.101515 [47] train-error:0.098485 [48] train-error:0.100000 [49] train-error:0.096970 [50] train-error:0.095455 [51] train-error:0.092424 [52] train-error:0.090909 [53] train-error:0.089394 [54] train-error:0.089394 [55] train-error:0.089394 [56] train-error:0.084848 [57] train-error:0.087879 [58] train-error:0.090909 [59] train-error:0.092424 [60] train-error:0.095455 [61] train-error:0.080303 [62] train-error:0.081818 [63] train-error:0.083333 [64] train-error:0.083333 [65] train-error:0.078788 [66] train-error:0.078788 [67] train-error:0.077273 [68] train-error:0.075758 [69] train-error:0.074242 [70] train-error:0.075758 [71] train-error:0.074242 [72] train-error:0.074242 [73] train-error:0.071212 [74] train-error:0.068182 [75] train-error:0.068182 [76] train-error:0.071212 [77] train-error:0.069697 [78] train-error:0.071212 [79] train-error:0.066667 [80] train-error:0.072727 [81] train-error:0.065152 [82] train-error:0.072727 [83] train-error:0.071212 [84] train-error:0.071212 [85] train-error:0.066667 [86] train-error:0.065152 [87] train-error:0.065152 [88] train-error:0.060606 [89] train-error:0.060606 [90] train-error:0.057576 [91] train-error:0.059091 [92] train-error:0.057576 [93] train-error:0.056061 [94] train-error:0.057576 [95] train-error:0.057576 [96] train-error:0.056061 [97] train-error:0.054545 [98] train-error:0.057576 [99] train-error:0.054545 [100] train-error:0.054545 [1] train-error:0.269697 [2] train-error:0.221212 [3] train-error:0.195455 [4] train-error:0.200000 [5] train-error:0.183333 [6] train-error:0.183333 [7] train-error:0.180303 [8] train-error:0.183333 [9] train-error:0.180303 [10] train-error:0.175758 [11] train-error:0.171212 [12] train-error:0.163636 [13] train-error:0.163636 [14] train-error:0.153030 [15] train-error:0.156061 [16] train-error:0.142424 [17] train-error:0.140909 [18] train-error:0.143939 [19] train-error:0.145455 [20] train-error:0.139394 [21] train-error:0.136364 [22] train-error:0.134848 [23] train-error:0.128788 [24] train-error:0.119697 [25] train-error:0.118182 [26] train-error:0.122727 [27] train-error:0.118182 [28] train-error:0.112121 [29] train-error:0.107576 [30] train-error:0.104545 [31] train-error:0.109091 [32] train-error:0.101515 [33] train-error:0.096970 [34] train-error:0.101515 [35] train-error:0.100000 [36] train-error:0.096970 [37] train-error:0.095455 [38] train-error:0.090909 [39] train-error:0.093939 [40] train-error:0.096970 [41] train-error:0.089394 [42] train-error:0.090909 [43] train-error:0.092424 [44] train-error:0.089394 [45] train-error:0.083333 [46] train-error:0.089394 [47] train-error:0.087879 [48] train-error:0.081818 [49] train-error:0.077273 [50] train-error:0.078788 [51] train-error:0.083333 [52] train-error:0.080303 [53] train-error:0.078788 [54] train-error:0.075758 [55] train-error:0.074242 [56] train-error:0.071212 [57] train-error:0.068182 [58] train-error:0.069697 [59] train-error:0.069697 [60] train-error:0.068182 [61] train-error:0.066667 [62] train-error:0.068182 [63] train-error:0.074242 [64] train-error:0.072727 [65] train-error:0.071212 [66] train-error:0.068182 [67] train-error:0.072727 [68] train-error:0.068182 [69] train-error:0.065152 [70] train-error:0.059091 [71] train-error:0.060606 [72] train-error:0.060606 [73] train-error:0.062121 [74] train-error:0.060606 [75] train-error:0.063636 [76] train-error:0.059091 [77] train-error:0.062121 [78] train-error:0.060606 [79] train-error:0.059091 [80] train-error:0.059091 [81] train-error:0.057576 [82] train-error:0.056061 [83] train-error:0.057576 [84] train-error:0.057576 [85] train-error:0.057576 [86] train-error:0.056061 [87] train-error:0.054545 [88] train-error:0.056061 [89] train-error:0.053030 [90] train-error:0.053030 [91] train-error:0.048485 [92] train-error:0.051515 [93] train-error:0.053030 [94] train-error:0.050000 [95] train-error:0.050000 [96] train-error:0.050000 [97] train-error:0.048485 [98] train-error:0.053030 [99] train-error:0.051515 [100] train-error:0.050000 [1] train-error:0.250000 [2] train-error:0.240909 [3] train-error:0.227273 [4] train-error:0.204545 [5] train-error:0.189394 [6] train-error:0.196970 [7] train-error:0.181818 [8] train-error:0.175758 [9] train-error:0.162121 [10] train-error:0.157576 [11] train-error:0.159091 [12] train-error:0.163636 [13] train-error:0.157576 [14] train-error:0.154545 [15] train-error:0.148485 [16] train-error:0.145455 [17] train-error:0.145455 [18] train-error:0.143939 [19] train-error:0.139394 [20] train-error:0.139394 [21] train-error:0.130303 [22] train-error:0.131818 [23] train-error:0.128788 [24] train-error:0.133333 [25] train-error:0.127273 [26] train-error:0.122727 [27] train-error:0.121212 [28] train-error:0.119697 [29] train-error:0.122727 [30] train-error:0.124242 [31] train-error:0.121212 [32] train-error:0.115152 [33] train-error:0.116667 [34] train-error:0.109091 [35] train-error:0.109091 [36] train-error:0.109091 [37] train-error:0.113636 [38] train-error:0.106061 [39] train-error:0.101515 [40] train-error:0.101515 [41] train-error:0.104545 [42] train-error:0.103030 [43] train-error:0.101515 [44] train-error:0.096970 [45] train-error:0.098485 [46] train-error:0.089394 [47] train-error:0.081818 [48] train-error:0.089394 [49] train-error:0.087879 [50] train-error:0.086364 [51] train-error:0.087879 [52] train-error:0.090909 [53] train-error:0.083333 [54] train-error:0.089394 [55] train-error:0.087879 [56] train-error:0.081818 [57] train-error:0.078788 [58] train-error:0.081818 [59] train-error:0.075758 [60] train-error:0.078788 [61] train-error:0.078788 [62] train-error:0.075758 [63] train-error:0.077273 [64] train-error:0.072727 [65] train-error:0.077273 [66] train-error:0.080303 [67] train-error:0.077273 [68] train-error:0.080303 [69] train-error:0.080303 [70] train-error:0.077273 [71] train-error:0.072727 [72] train-error:0.071212 [73] train-error:0.071212 [74] train-error:0.066667 [75] train-error:0.066667 [76] train-error:0.065152 [77] train-error:0.065152 [78] train-error:0.065152 [79] train-error:0.063636 [80] train-error:0.069697 [81] train-error:0.066667 [82] train-error:0.066667 [83] train-error:0.066667 [84] train-error:0.068182 [85] train-error:0.065152 [86] train-error:0.060606 [87] train-error:0.059091 [88] train-error:0.059091 [89] train-error:0.056061 [90] train-error:0.059091 [91] train-error:0.059091 [92] train-error:0.056061 [93] train-error:0.053030 [94] train-error:0.053030 [95] train-error:0.053030 [96] train-error:0.050000 [97] train-error:0.051515 [98] train-error:0.053030 [99] train-error:0.051515 [100] train-error:0.051515
[Tune-y] 7: acc.test.mean=0.8412121; time: 0.0 min [Tune-x] 8: max_depth=8; min_child_weight=7.08; subsample=0.986; colsample_bytree=0.952
[1] train-error:0.215152 [2] train-error:0.189394 [3] train-error:0.187879 [4] train-error:0.169697 [5] train-error:0.160606 [6] train-error:0.145455 [7] train-error:0.151515 [8] train-error:0.150000 [9] train-error:0.140909 [10] train-error:0.140909 [11] train-error:0.139394 [12] train-error:0.124242 [13] train-error:0.122727 [14] train-error:0.121212 [15] train-error:0.112121 [16] train-error:0.104545 [17] train-error:0.101515 [18] train-error:0.100000 [19] train-error:0.090909 [20] train-error:0.100000 [21] train-error:0.092424 [22] train-error:0.089394 [23] train-error:0.090909 [24] train-error:0.081818 [25] train-error:0.081818 [26] train-error:0.077273 [27] train-error:0.071212 [28] train-error:0.069697 [29] train-error:0.066667 [30] train-error:0.063636 [31] train-error:0.062121 [32] train-error:0.060606 [33] train-error:0.057576 [34] train-error:0.060606 [35] train-error:0.056061 [36] train-error:0.054545 [37] train-error:0.057576 [38] train-error:0.057576 [39] train-error:0.054545 [40] train-error:0.053030 [41] train-error:0.050000 [42] train-error:0.045455 [43] train-error:0.040909 [44] train-error:0.039394 [45] train-error:0.037879 [46] train-error:0.036364 [47] train-error:0.037879 [48] train-error:0.039394 [49] train-error:0.039394 [50] train-error:0.036364 [51] train-error:0.028788 [52] train-error:0.028788 [53] train-error:0.030303 [54] train-error:0.028788 [55] train-error:0.028788 [56] train-error:0.027273 [57] train-error:0.025758 [58] train-error:0.028788 [59] train-error:0.025758 [60] train-error:0.025758 [61] train-error:0.025758 [62] train-error:0.025758 [63] train-error:0.025758 [64] train-error:0.025758 [65] train-error:0.025758 [66] train-error:0.022727 [67] train-error:0.021212 [68] train-error:0.024242 [69] train-error:0.019697 [70] train-error:0.019697 [71] train-error:0.021212 [72] train-error:0.019697 [73] train-error:0.019697 [74] train-error:0.019697 [75] train-error:0.018182 [76] train-error:0.018182 [77] train-error:0.018182 [78] train-error:0.018182 [79] train-error:0.018182 [80] train-error:0.016667 [81] train-error:0.018182 [82] train-error:0.016667 [83] train-error:0.019697 [84] train-error:0.019697 [85] train-error:0.018182 [86] train-error:0.019697 [87] train-error:0.015152 [88] train-error:0.015152 [89] train-error:0.013636 [90] train-error:0.015152 [91] train-error:0.015152 [92] train-error:0.013636 [93] train-error:0.013636 [94] train-error:0.013636 [95] train-error:0.013636 [96] train-error:0.012121 [97] train-error:0.012121 [98] train-error:0.012121 [99] train-error:0.012121 [100] train-error:0.012121 [1] train-error:0.233333 [2] train-error:0.201515 [3] train-error:0.196970 [4] train-error:0.177273 [5] train-error:0.181818 [6] train-error:0.177273 [7] train-error:0.165152 [8] train-error:0.160606 [9] train-error:0.156061 [10] train-error:0.148485 [11] train-error:0.137879 [12] train-error:0.133333 [13] train-error:0.127273 [14] train-error:0.116667 [15] train-error:0.113636 [16] train-error:0.106061 [17] train-error:0.106061 [18] train-error:0.101515 [19] train-error:0.092424 [20] train-error:0.089394 [21] train-error:0.081818 [22] train-error:0.084848 [23] train-error:0.077273 [24] train-error:0.072727 [25] train-error:0.078788 [26] train-error:0.077273 [27] train-error:0.071212 [28] train-error:0.066667 [29] train-error:0.063636 [30] train-error:0.054545 [31] train-error:0.054545 [32] train-error:0.057576 [33] train-error:0.054545 [34] train-error:0.046970 [35] train-error:0.046970 [36] train-error:0.045455 [37] train-error:0.045455 [38] train-error:0.045455 [39] train-error:0.045455 [40] train-error:0.043939 [41] train-error:0.040909 [42] train-error:0.037879 [43] train-error:0.036364 [44] train-error:0.037879 [45] train-error:0.039394 [46] train-error:0.033333 [47] train-error:0.037879 [48] train-error:0.034848 [49] train-error:0.030303 [50] train-error:0.030303 [51] train-error:0.028788 [52] train-error:0.030303 [53] train-error:0.030303 [54] train-error:0.030303 [55] train-error:0.028788 [56] train-error:0.027273 [57] train-error:0.027273 [58] train-error:0.025758 [59] train-error:0.022727 [60] train-error:0.022727 [61] train-error:0.022727 [62] train-error:0.021212 [63] train-error:0.021212 [64] train-error:0.021212 [65] train-error:0.024242 [66] train-error:0.021212 [67] train-error:0.022727 [68] train-error:0.022727 [69] train-error:0.018182 [70] train-error:0.018182 [71] train-error:0.015152 [72] train-error:0.015152 [73] train-error:0.015152 [74] train-error:0.015152 [75] train-error:0.016667 [76] train-error:0.015152 [77] train-error:0.016667 [78] train-error:0.015152 [79] train-error:0.015152 [80] train-error:0.016667 [81] train-error:0.016667 [82] train-error:0.018182 [83] train-error:0.018182 [84] train-error:0.018182 [85] train-error:0.018182 [86] train-error:0.016667 [87] train-error:0.016667 [88] train-error:0.018182 [89] train-error:0.016667 [90] train-error:0.016667 [91] train-error:0.016667 [92] train-error:0.016667 [93] train-error:0.016667 [94] train-error:0.016667 [95] train-error:0.016667 [96] train-error:0.016667 [97] train-error:0.016667 [98] train-error:0.016667 [99] train-error:0.015152 [100] train-error:0.016667 [1] train-error:0.231818 [2] train-error:0.210606 [3] train-error:0.203030 [4] train-error:0.196970 [5] train-error:0.171212 [6] train-error:0.160606 [7] train-error:0.148485 [8] train-error:0.142424 [9] train-error:0.136364 [10] train-error:0.136364 [11] train-error:0.124242 [12] train-error:0.116667 [13] train-error:0.119697 [14] train-error:0.103030 [15] train-error:0.100000 [16] train-error:0.089394 [17] train-error:0.084848 [18] train-error:0.081818 [19] train-error:0.077273 [20] train-error:0.080303 [21] train-error:0.084848 [22] train-error:0.080303 [23] train-error:0.075758 [24] train-error:0.066667 [25] train-error:0.072727 [26] train-error:0.066667 [27] train-error:0.068182 [28] train-error:0.065152 [29] train-error:0.063636 [30] train-error:0.066667 [31] train-error:0.062121 [32] train-error:0.060606 [33] train-error:0.060606 [34] train-error:0.057576 [35] train-error:0.050000 [36] train-error:0.048485 [37] train-error:0.051515 [38] train-error:0.048485 [39] train-error:0.050000 [40] train-error:0.046970 [41] train-error:0.040909 [42] train-error:0.039394 [43] train-error:0.037879 [44] train-error:0.036364 [45] train-error:0.031818 [46] train-error:0.036364 [47] train-error:0.031818 [48] train-error:0.033333 [49] train-error:0.034848 [50] train-error:0.027273 [51] train-error:0.030303 [52] train-error:0.028788 [53] train-error:0.028788 [54] train-error:0.028788 [55] train-error:0.031818 [56] train-error:0.030303 [57] train-error:0.027273 [58] train-error:0.027273 [59] train-error:0.025758 [60] train-error:0.025758 [61] train-error:0.024242 [62] train-error:0.024242 [63] train-error:0.024242 [64] train-error:0.025758 [65] train-error:0.024242 [66] train-error:0.024242 [67] train-error:0.024242 [68] train-error:0.022727 [69] train-error:0.022727 [70] train-error:0.022727 [71] train-error:0.024242 [72] train-error:0.021212 [73] train-error:0.021212 [74] train-error:0.021212 [75] train-error:0.021212 [76] train-error:0.021212 [77] train-error:0.021212 [78] train-error:0.021212 [79] train-error:0.021212 [80] train-error:0.021212 [81] train-error:0.019697 [82] train-error:0.018182 [83] train-error:0.019697 [84] train-error:0.018182 [85] train-error:0.018182 [86] train-error:0.018182 [87] train-error:0.018182 [88] train-error:0.018182 [89] train-error:0.018182 [90] train-error:0.018182 [91] train-error:0.018182 [92] train-error:0.018182 [93] train-error:0.018182 [94] train-error:0.018182 [95] train-error:0.018182 [96] train-error:0.018182 [97] train-error:0.018182 [98] train-error:0.018182 [99] train-error:0.018182 [100] train-error:0.018182 [1] train-error:0.230303 [2] train-error:0.184848 [3] train-error:0.183333 [4] train-error:0.177273 [5] train-error:0.177273 [6] train-error:0.166667 [7] train-error:0.165152 [8] train-error:0.156061 [9] train-error:0.143939 [10] train-error:0.140909 [11] train-error:0.143939 [12] train-error:0.139394 [13] train-error:0.133333 [14] train-error:0.130303 [15] train-error:0.119697 [16] train-error:0.116667 [17] train-error:0.110606 [18] train-error:0.109091 [19] train-error:0.104545 [20] train-error:0.095455 [21] train-error:0.095455 [22] train-error:0.081818 [23] train-error:0.083333 [24] train-error:0.078788 [25] train-error:0.071212 [26] train-error:0.071212 [27] train-error:0.066667 [28] train-error:0.065152 [29] train-error:0.065152 [30] train-error:0.060606 [31] train-error:0.053030 [32] train-error:0.056061 [33] train-error:0.054545 [34] train-error:0.051515 [35] train-error:0.053030 [36] train-error:0.051515 [37] train-error:0.042424 [38] train-error:0.045455 [39] train-error:0.046970 [40] train-error:0.043939 [41] train-error:0.043939 [42] train-error:0.046970 [43] train-error:0.043939 [44] train-error:0.043939 [45] train-error:0.042424 [46] train-error:0.043939 [47] train-error:0.042424 [48] train-error:0.040909 [49] train-error:0.037879 [50] train-error:0.034848 [51] train-error:0.034848 [52] train-error:0.039394 [53] train-error:0.036364 [54] train-error:0.031818 [55] train-error:0.030303 [56] train-error:0.031818 [57] train-error:0.027273 [58] train-error:0.028788 [59] train-error:0.027273 [60] train-error:0.025758 [61] train-error:0.025758 [62] train-error:0.027273 [63] train-error:0.025758 [64] train-error:0.024242 [65] train-error:0.025758 [66] train-error:0.025758 [67] train-error:0.025758 [68] train-error:0.024242 [69] train-error:0.024242 [70] train-error:0.024242 [71] train-error:0.022727 [72] train-error:0.022727 [73] train-error:0.021212 [74] train-error:0.022727 [75] train-error:0.019697 [76] train-error:0.019697 [77] train-error:0.019697 [78] train-error:0.021212 [79] train-error:0.021212 [80] train-error:0.019697 [81] train-error:0.019697 [82] train-error:0.019697 [83] train-error:0.018182 [84] train-error:0.019697 [85] train-error:0.018182 [86] train-error:0.018182 [87] train-error:0.018182 [88] train-error:0.016667 [89] train-error:0.016667 [90] train-error:0.016667 [91] train-error:0.015152 [92] train-error:0.015152 [93] train-error:0.015152 [94] train-error:0.015152 [95] train-error:0.015152 [96] train-error:0.015152 [97] train-error:0.016667 [98] train-error:0.013636 [99] train-error:0.012121 [100] train-error:0.013636 [1] train-error:0.216667 [2] train-error:0.186364 [3] train-error:0.175758 [4] train-error:0.174242 [5] train-error:0.159091 [6] train-error:0.165152 [7] train-error:0.142424 [8] train-error:0.140909 [9] train-error:0.137879 [10] train-error:0.136364 [11] train-error:0.134848 [12] train-error:0.115152 [13] train-error:0.121212 [14] train-error:0.115152 [15] train-error:0.112121 [16] train-error:0.110606 [17] train-error:0.100000 [18] train-error:0.095455 [19] train-error:0.093939 [20] train-error:0.095455 [21] train-error:0.092424 [22] train-error:0.086364 [23] train-error:0.078788 [24] train-error:0.069697 [25] train-error:0.069697 [26] train-error:0.068182 [27] train-error:0.068182 [28] train-error:0.066667 [29] train-error:0.060606 [30] train-error:0.059091 [31] train-error:0.060606 [32] train-error:0.060606 [33] train-error:0.060606 [34] train-error:0.059091 [35] train-error:0.050000 [36] train-error:0.051515 [37] train-error:0.050000 [38] train-error:0.048485 [39] train-error:0.051515 [40] train-error:0.043939 [41] train-error:0.043939 [42] train-error:0.043939 [43] train-error:0.043939 [44] train-error:0.048485 [45] train-error:0.045455 [46] train-error:0.042424 [47] train-error:0.042424 [48] train-error:0.042424 [49] train-error:0.042424 [50] train-error:0.040909 [51] train-error:0.043939 [52] train-error:0.040909 [53] train-error:0.040909 [54] train-error:0.040909 [55] train-error:0.037879 [56] train-error:0.036364 [57] train-error:0.037879 [58] train-error:0.036364 [59] train-error:0.039394 [60] train-error:0.039394 [61] train-error:0.034848 [62] train-error:0.037879 [63] train-error:0.037879 [64] train-error:0.036364 [65] train-error:0.037879 [66] train-error:0.034848 [67] train-error:0.033333 [68] train-error:0.036364 [69] train-error:0.034848 [70] train-error:0.033333 [71] train-error:0.033333 [72] train-error:0.033333 [73] train-error:0.033333 [74] train-error:0.028788 [75] train-error:0.027273 [76] train-error:0.028788 [77] train-error:0.025758 [78] train-error:0.027273 [79] train-error:0.027273 [80] train-error:0.027273 [81] train-error:0.025758 [82] train-error:0.022727 [83] train-error:0.021212 [84] train-error:0.022727 [85] train-error:0.024242 [86] train-error:0.021212 [87] train-error:0.021212 [88] train-error:0.021212 [89] train-error:0.021212 [90] train-error:0.022727 [91] train-error:0.022727 [92] train-error:0.022727 [93] train-error:0.016667 [94] train-error:0.019697 [95] train-error:0.018182 [96] train-error:0.018182 [97] train-error:0.018182 [98] train-error:0.018182 [99] train-error:0.019697 [100] train-error:0.018182
[Tune-y] 8: acc.test.mean=0.8727273; time: 0.0 min [Tune-x] 9: max_depth=9; min_child_weight=4.68; subsample=0.877; colsample_bytree=0.711
[1] train-error:0.227273 [2] train-error:0.184848 [3] train-error:0.175758 [4] train-error:0.174242 [5] train-error:0.142424 [6] train-error:0.140909 [7] train-error:0.130303 [8] train-error:0.133333 [9] train-error:0.122727 [10] train-error:0.115152 [11] train-error:0.112121 [12] train-error:0.107576 [13] train-error:0.100000 [14] train-error:0.103030 [15] train-error:0.093939 [16] train-error:0.089394 [17] train-error:0.086364 [18] train-error:0.078788 [19] train-error:0.077273 [20] train-error:0.075758 [21] train-error:0.075758 [22] train-error:0.077273 [23] train-error:0.071212 [24] train-error:0.065152 [25] train-error:0.068182 [26] train-error:0.066667 [27] train-error:0.063636 [28] train-error:0.057576 [29] train-error:0.051515 [30] train-error:0.043939 [31] train-error:0.046970 [32] train-error:0.043939 [33] train-error:0.043939 [34] train-error:0.042424 [35] train-error:0.037879 [36] train-error:0.034848 [37] train-error:0.037879 [38] train-error:0.030303 [39] train-error:0.031818 [40] train-error:0.025758 [41] train-error:0.025758 [42] train-error:0.022727 [43] train-error:0.019697 [44] train-error:0.021212 [45] train-error:0.021212 [46] train-error:0.021212 [47] train-error:0.022727 [48] train-error:0.019697 [49] train-error:0.019697 [50] train-error:0.018182 [51] train-error:0.016667 [52] train-error:0.018182 [53] train-error:0.016667 [54] train-error:0.013636 [55] train-error:0.013636 [56] train-error:0.012121 [57] train-error:0.010606 [58] train-error:0.010606 [59] train-error:0.010606 [60] train-error:0.012121 [61] train-error:0.009091 [62] train-error:0.012121 [63] train-error:0.012121 [64] train-error:0.010606 [65] train-error:0.012121 [66] train-error:0.010606 [67] train-error:0.010606 [68] train-error:0.012121 [69] train-error:0.010606 [70] train-error:0.007576 [71] train-error:0.007576 [72] train-error:0.007576 [73] train-error:0.006061 [74] train-error:0.006061 [75] train-error:0.006061 [76] train-error:0.007576 [77] 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[31] train-error:0.045455 [32] train-error:0.042424 [33] train-error:0.040909 [34] train-error:0.039394 [35] train-error:0.040909 [36] train-error:0.037879 [37] train-error:0.034848 [38] train-error:0.037879 [39] train-error:0.036364 [40] train-error:0.033333 [41] train-error:0.034848 [42] train-error:0.033333 [43] train-error:0.030303 [44] train-error:0.027273 [45] train-error:0.028788 [46] train-error:0.027273 [47] train-error:0.025758 [48] train-error:0.024242 [49] train-error:0.025758 [50] train-error:0.027273 [51] train-error:0.027273 [52] train-error:0.027273 [53] train-error:0.027273 [54] train-error:0.025758 [55] train-error:0.021212 [56] train-error:0.024242 [57] train-error:0.025758 [58] train-error:0.022727 [59] train-error:0.021212 [60] train-error:0.022727 [61] train-error:0.021212 [62] train-error:0.019697 [63] train-error:0.022727 [64] train-error:0.021212 [65] train-error:0.018182 [66] train-error:0.018182 [67] train-error:0.015152 [68] train-error:0.013636 [69] 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[22] train-error:0.060606 [23] train-error:0.068182 [24] train-error:0.062121 [25] train-error:0.060606 [26] train-error:0.057576 [27] train-error:0.059091 [28] train-error:0.059091 [29] train-error:0.054545 [30] train-error:0.051515 [31] train-error:0.050000 [32] train-error:0.048485 [33] train-error:0.045455 [34] train-error:0.045455 [35] train-error:0.042424 [36] train-error:0.036364 [37] train-error:0.037879 [38] train-error:0.028788 [39] train-error:0.030303 [40] train-error:0.030303 [41] train-error:0.028788 [42] train-error:0.025758 [43] train-error:0.022727 [44] train-error:0.022727 [45] train-error:0.024242 [46] train-error:0.025758 [47] train-error:0.021212 [48] train-error:0.021212 [49] train-error:0.022727 [50] train-error:0.022727 [51] train-error:0.021212 [52] train-error:0.021212 [53] train-error:0.021212 [54] train-error:0.021212 [55] train-error:0.021212 [56] train-error:0.019697 [57] train-error:0.021212 [58] train-error:0.019697 [59] train-error:0.016667 [60] 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train-error:0.003030 [99] train-error:0.003030 [100] train-error:0.003030
[Tune-y] 9: acc.test.mean=0.8933333; time: 0.0 min [Tune-x] 10: max_depth=7; min_child_weight=1.13; subsample=0.888; colsample_bytree=0.875
[1] train-error:0.118182 [2] train-error:0.081818 [3] train-error:0.059091 [4] train-error:0.056061 [5] train-error:0.039394 [6] train-error:0.037879 [7] train-error:0.037879 [8] train-error:0.034848 [9] train-error:0.028788 [10] train-error:0.022727 [11] train-error:0.027273 [12] train-error:0.022727 [13] train-error:0.021212 [14] train-error:0.018182 [15] train-error:0.019697 [16] train-error:0.016667 [17] train-error:0.012121 [18] train-error:0.012121 [19] train-error:0.013636 [20] train-error:0.010606 [21] train-error:0.010606 [22] train-error:0.007576 [23] train-error:0.006061 [24] train-error:0.006061 [25] train-error:0.006061 [26] train-error:0.006061 [27] train-error:0.003030 [28] train-error:0.003030 [29] train-error:0.003030 [30] train-error:0.004545 [31] train-error:0.004545 [32] train-error:0.004545 [33] train-error:0.000000 [34] train-error:0.000000 [35] train-error:0.000000 [36] train-error:0.000000 [37] train-error:0.000000 [38] train-error:0.000000 [39] 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train-error:0.045455 [8] train-error:0.039394 [9] train-error:0.033333 [10] train-error:0.027273 [11] train-error:0.027273 [12] train-error:0.025758 [13] train-error:0.024242 [14] train-error:0.021212 [15] train-error:0.018182 [16] train-error:0.015152 [17] train-error:0.013636 [18] train-error:0.013636 [19] train-error:0.013636 [20] train-error:0.012121 [21] train-error:0.009091 [22] train-error:0.009091 [23] train-error:0.013636 [24] train-error:0.010606 [25] train-error:0.009091 [26] train-error:0.010606 [27] train-error:0.004545 [28] train-error:0.004545 [29] train-error:0.004545 [30] train-error:0.004545 [31] train-error:0.001515 [32] train-error:0.001515 [33] train-error:0.001515 [34] train-error:0.001515 [35] train-error:0.001515 [36] train-error:0.000000 [37] train-error:0.000000 [38] train-error:0.000000 [39] train-error:0.000000 [40] train-error:0.000000 [41] train-error:0.000000 [42] train-error:0.000000 [43] train-error:0.000000 [44] train-error:0.000000 [45] 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[22] train-error:0.012121 [23] train-error:0.010606 [24] train-error:0.009091 [25] train-error:0.007576 [26] train-error:0.004545 [27] train-error:0.004545 [28] train-error:0.004545 [29] train-error:0.007576 [30] train-error:0.003030 [31] train-error:0.003030 [32] train-error:0.003030 [33] train-error:0.003030 [34] train-error:0.003030 [35] train-error:0.003030 [36] train-error:0.000000 [37] train-error:0.000000 [38] train-error:0.000000 [39] train-error:0.000000 [40] train-error:0.000000 [41] train-error:0.000000 [42] train-error:0.000000 [43] train-error:0.000000 [44] train-error:0.000000 [45] train-error:0.000000 [46] train-error:0.000000 [47] train-error:0.000000 [48] train-error:0.000000 [49] train-error:0.000000 [50] train-error:0.000000 [51] train-error:0.000000 [52] train-error:0.000000 [53] train-error:0.000000 [54] train-error:0.000000 [55] train-error:0.000000 [56] train-error:0.000000 [57] train-error:0.000000 [58] train-error:0.000000 [59] train-error:0.000000 [60] 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train-error:0.000000 [99] train-error:0.000000 [100] train-error:0.000000
[Tune-y] 10: acc.test.mean=0.8921212; time: 0.0 min [Tune] Result: max_depth=9; min_child_weight=4.68; subsample=0.877; colsample_bytree=0.711 : acc.test.mean=0.8933333
setDT(smote.tuned.train) #set train set to data table
setDT(smote.tuned.test) #set test set to data table
smote.tuned.train_label <- smote.tuned.train$Attrition # assign target label for train set to a new field smote.tuned.train_label
smote.tuned.test_label <- smote.tuned.test$Attrition # assign test label for train set to a new field smote.tuned.test_label
smote.tuned.new_train <- model.matrix(~.+0,data = smote.tuned.train[,-c("Attrition"),with=F]) # convert smote.tuned.train to matrix format and assign to new variable smote.tuned.new_train
smote.tuned.new_test <- model.matrix(~.+0,data = smote.tuned.test[,-c("Attrition"),with=F]) # convert smote.tuned.test to matrix format and assign to new variable smote.tuned.new_test
smote.tuned.train_label <- as.numeric(smote.tuned.train_label)-1 #convert smote.tuned.train_label to numeric - 1 so we have (0,1) instead of(1,2)
smote.tuned.test_label <- as.numeric(smote.tuned.test_label)-1 #convert smote.tuned.train_label to numeric - 1 so we have (0,1) instead of(1,2)
smote.tuned.dtrain <- xgb.DMatrix(data = smote.tuned.new_train,label = smote.tuned.train_label ) #Construct xgb.DMatrix object from train set and assign to smote.tuned.dtrain
smote.tuned.dtest <- xgb.DMatrix(data = smote.tuned.new_test,label = smote.tuned.test_label) #Construct xgb.DMatrix object frrom test set and assign to smote.tuned.dtest
#assign parameters based on best paramaters from hypertune/cross validation
smote.paramsXG <- list(booster = 'gbtree', objective = "binary:logistic",
eta=0.3, gamma=0, max_depth= smotetuneXG$x$max_depth,
min_child_weight=smotetuneXG$x$min_child_weight, subsample=smotetuneXG$x$subsample,
colsample_bytree= smotetuneXG$x$colsample_bytree)
#Train hypertuned xgb model using assigned params previously defined
smote.tuned.xgb <- xgb.train (params = smote.paramsXG, data = smote.tuned.dtrain, nrounds = 79,
watchlist = list(val=smote.tuned.dtest,train=smote.tuned.dtrain), print_every_n = 10,
early_stopping_rounds = 10, maximize = F , eval_metric = "error")
[1] val-error:0.393665 train-error:0.225455 Multiple eval metrics are present. Will use train_error for early stopping. Will train until train_error hasn't improved in 10 rounds. [11] val-error:0.278281 train-error:0.044848 [21] val-error:0.276018 train-error:0.013333 [31] val-error:0.269231 train-error:0.007273 [41] val-error:0.278281 train-error:0.004848 [51] val-error:0.260181 train-error:0.002424 Stopping. Best iteration: [43] val-error:0.273756 train-error:0.002424
smote.tuned.xgbpred <- predict (smote.tuned.xgb,smote.tuned.dtest) #predict model using test set
smote.tuned.xgbpred <- ifelse(smote.tuned.xgbpred > 0.5,1,0) #if probablity > 0.5 set prediction to class 1 else set predictionto class 0
#Feature importance plot for base xgboost model
smote.tuned.mat <- xgb.importance (feature_names = colnames(smote.tuned.new_train),model = smote.tuned.xgb)
xgb.plot.importance (importance_matrix = smote.tuned.mat[1:10] , rel_to_first = TRUE, xlab = "Relative importance Hypertuned XGBOOST with Smote" )
smote.tuned.test$Attrition <- as.numeric(smote.tuned.test$Attrition)-1
options(repr.plot.width=8, repr.plot.height=6)
#store table results in conf_df
conf_df <- data.frame(table(smote.tuned.test$Attrition, smote.tuned.xgbpred))
#plotting of confusion matrix using ggplot2 library
ggplot(data = conf_df, mapping = aes(x = smote.tuned.xgbpred, y = Var1)) +
geom_tile(aes(fill = Freq), colour = "white") +
geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1) +
scale_fill_gradient(low = "#F3F781", high = "#58FA82") + theme(legend.position="none", strip.background = element_blank(), strip.text.x = element_blank(),
plot.title=element_text(hjust=0.5, color="white"), plot.subtitle=element_text(colour="white"), plot.background=element_rect(fill="#0D7680"),
axis.text.x=element_text(colour="white"), axis.text.y=element_text(colour="white"),
axis.title=element_text(colour="white"),
legend.background = element_rect(fill="#FFF9F5",
size=0.5, linetype="solid",
colour ="black")) +
labs(title="Confusion Matrix SMOTE Hypertuned XGBOOST", y="Actual Attrrition", x="Predicted Attrition")
xgb.plot.tree(model=smote.tuned.xgb, tree=0)
xgb.plot.tree(model=smote.tuned.xgb, tree=1)
#store table of predicted and actual values
smote.tuned.cmxgboost<- table(smote.tuned.test$Attrition, smote.tuned.xgbpred)
smote.xgboost.hypertunedaccuracy <- model.accuracy(smote.tuned.cmxgboost)
#apply recall formula on yes cases of attrtiion
smote.xgboost.hypertunedrecallYes <- AttritionYes.recall(smote.tuned.cmxgboost)
#apply precision formula on yes cases of attrtiion
smote.xgboost.hypertunedprecisionYes <- AttritionYes.precision(smote.tuned.cmxgboost)
#calculate F1 Score for yes cases of attrtiion
smote.xgboost.hypertuned_F1ScoreYes <- round((2 * smote.xgboost.hypertunedprecisionYes * smote.xgboost.hypertunedrecallYes) / (smote.xgboost.hypertunedrecallYes
+smote.xgboost.hypertunedprecisionYes) ,digits=2)
#apply recall formula on no cases of attrtiion
smote.xgboost.hypertunedrecallNo <- AttritionNo.recall(smote.tuned.cmxgboost)
#apply precision formula on no cases of attrtiion
smote.xgboost.hypertunedprecisionNo <- AttritionNo.precision(smote.tuned.cmxgboost)
#calculate F1 Score for no cases of attrtiion
smote.xgboost.hypertuned_F1ScoreNo <- round((2 * smote.xgboost.hypertunedprecisionNo * smote.xgboost.hypertunedrecallNo) / (smote.xgboost.hypertunedrecallNo
+smote.xgboost.hypertunedprecisionNo) ,digits=2)
total.recall.smote.hypertuned_XGBoost<- total.recall(smote.tuned.cmxgboost) #apply total recall formula (both cases of attrtiion)
total.precision.smote.hypertunedXGBoost <- total.precision(smote.tuned.cmxgboost) #apply total precision formula (both cases of attrtiion)
total.F1Score.smote.hypertunedXGBoost<- round((smote.xgboost.hypertuned_F1ScoreYes +
smote.xgboost.hypertuned_F1ScoreNo)/2,digits=2) #calculate total F1 Score (both cases of attrtiion)
BaseModelAttrition <- c("Base Decision Tree", "HyperTuned/PrePruned Decision Tree", 'PostPruned Base Decison Tree',
"Base XGBOOST", "HyperTuned XGBOOST",
"HyperTuned KNN")
Accuracy <- c(dectree.baseaccuracy, dectree.hypertunedaccuracy,postprunetree.baseaccuracy,xgboost.baseaccuracy, xgboost.hypertunedaccuracy, knn.hypertunedaccuracy )
Recall <- c(total.recall.basetree, total.recall.hypertunedtree,total.recall.postprunetree ,total.recall.base_XGBoost,total.recall.hypertuned_XGBoost,total.recall.hypertuned_knn)
Precision <- c(total.precision.basetree, total.precision.hypertunedtree,total.precision.postprunetree,total.precision.base_XGBoost, total.precision.hypertunedXGBoost, total.precision.hypertuned_knn )
F1Score <- c(total.F1Score.basetree,total.F1Score.hypertunedtree,total.F1Score.postprunetree,total.F1Score.base_XGBoost,total.F1Score.hypertunedXGBoost,total.F1Scoreknn)
AttritionResults <- data.frame(BaseModelAttrition,Accuracy ,Recall , Precision , F1Score )
AttritionResults
| BaseModelAttrition | Accuracy | Recall | Precision | F1Score |
|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> |
| Base Decision Tree | 0.84 | 0.65 | 0.71 | 0.68 |
| HyperTuned/PrePruned Decision Tree | 0.85 | 0.56 | 0.77 | 0.63 |
| PostPruned Base Decison Tree | 0.84 | 0.49 | 0.77 | 0.56 |
| Base XGBOOST | 0.87 | 0.67 | 0.80 | 0.72 |
| HyperTuned XGBOOST | 0.86 | 0.65 | 0.78 | 0.70 |
| HyperTuned KNN | 0.85 | 0.47 | 0.94 | 0.55 |
SmoteModelAttrition <- c("Base Decision Tree", "HyperTuned/PrePruned Decision Tree", 'PostPruned Base Decison Tree',
"Base XGBOOST", "HyperTuned XGBOOST",
"HyperTuned KNN" )
Smote.Accuracy <- c(smote.dectree.baseaccuracy, smote.dectree.hypertunedaccuracy,smote.postprunetree.baseaccuracy,smote.xgboost.baseaccuracy, smote.xgboost.hypertunedaccuracy, smote.knn.hypertunedaccuracy )
Smote.Recall <- c(total.recall.smote_basetree, total.recall.smote.hypertunedtree,total.recall.smote.postprunetree,total.recall.smote.base_XGBoost, total.recall.smote.hypertuned_XGBoost,total.recall.smote_knn)
Smote.Precision <- c(total.precision.smote_basetree, total.precision.smote.hypertunedtree,total.precision.smote.postprunetree,total.precision.smote.base_XGBoost,total.precision.smote.hypertunedXGBoost, total.precision.smote_knn )
Smote.F1Score <- c(total.F1Score.smote_basetree,total.F1Score.smote.hypertunedtree,total.F1Score.smote.postprunetree,total.F1Score.smote.base_XGBoost,total.F1Score.smote.hypertunedXGBoost,total.F1Score.smote_knn)
AttritionSmoteResults <- data.frame(SmoteModelAttrition ,Smote.Accuracy ,Smote.Recall,Smote.Precision , Smote.F1Score )
AttritionSmoteResults
| SmoteModelAttrition | Smote.Accuracy | Smote.Recall | Smote.Precision | Smote.F1Score |
|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> |
| Base Decision Tree | 0.63 | 0.81 | 0.44 | 0.55 |
| HyperTuned/PrePruned Decision Tree | 0.65 | 0.82 | 0.46 | 0.58 |
| PostPruned Base Decison Tree | 0.67 | 0.81 | 0.47 | 0.59 |
| Base XGBOOST | 0.67 | 0.81 | 0.47 | 0.59 |
| HyperTuned XGBOOST | 0.73 | 0.83 | 0.53 | 0.64 |
| HyperTuned KNN | 0.54 | 0.75 | 0.37 | 0.48 |
BaseModelAttritionYes <- c("Base Decision Tree", "HyperTuned/PrePruned Decision Tree", 'PostPruned Base Decison Tree',
"Base XGBOOST", "HyperTuned XGBOOST",
"HyperTuned KNN")
Yes.Recall <- c(dectree.baserecallYes, dectree.hypertunedrecallYes,postprunetree.baserecallYes,xgboost.baserecallYes, xgboost.hypertunedrecallYes,knn.hypertunedrecallYes )
Yes.Precision <- c(dectree.baseprecisionYes, dectree.hypertunedprecisionYes,postprunetree.baseprecisionYes,xgboost.baseprecisionYes, xgboost.hypertunedprecisionYes, knn.hypertunedprecisionYes )
Yes.F1_Score<- c(dectree.base_F1ScoreYes, dectree.hypertuned_F1ScoreYes,postprunetree.base_F1ScoreYes,xgboost.base_F1ScoreYes, xgboost.hypertuned_F1ScoreYes, knn.hypertuned_F1ScoreYes )
AttritionYesResults <- data.frame(BaseModelAttritionYes, Yes.Recall , Yes.Precision , Yes.F1_Score )
AttritionYesResults
| BaseModelAttritionYes | Yes.Recall | Yes.Precision | Yes.F1_Score |
|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> |
| Base Decision Tree | 0.42 | 0.50 | 0.46 |
| HyperTuned/PrePruned Decision Tree | 0.25 | 0.58 | 0.35 |
| PostPruned Base Decison Tree | 0.12 | 0.56 | 0.20 |
| Base XGBOOST | 0.44 | 0.65 | 0.52 |
| HyperTuned XGBOOST | 0.40 | 0.60 | 0.48 |
| HyperTuned KNN | 0.10 | 0.88 | 0.18 |
BaseModelAttritionNo <- c("Base Decision Tree", "HyperTuned/PrePruned Decision Tree", 'PostPruned Base Decison Tree',
"Base XGBOOST", "HyperTuned XGBOOST",
"HyperTuned KNN")
No.Recall <- c(dectree.baserecallNo, dectree.hypertunedrecallNo,postprunetree.baserecallNo,xgboost.baserecallNo, xgboost.hypertunedrecallNo,knn.hypertunedrecallNo )
No.Precision <- c(dectree.baseprecisionNo, dectree.hypertunedprecisionNo,postprunetree.baseprecisionNo,xgboost.baseprecisionNo, xgboost.hypertunedprecisionNo, knn.hypertunedprecisionNo )
No.F1_Score<- c(dectree.base_F1ScoreNo, dectree.hypertuned_F1ScoreNo,postprunetree.base_F1ScoreNo,xgboost.base_F1ScoreNo, xgboost.hypertuned_F1ScoreNo, knn.hypertuned_F1ScoreNo )
AttritionNoResults <- data.frame(BaseModelAttritionNo ,No.Recall , No.Precision , No.F1_Score )
AttritionNoResults
| BaseModelAttritionNo | No.Recall | No.Precision | No.F1_Score |
|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> |
| Base Decision Tree | 0.89 | 0.92 | 0.90 |
| HyperTuned/PrePruned Decision Tree | 0.87 | 0.96 | 0.91 |
| PostPruned Base Decison Tree | 0.85 | 0.98 | 0.91 |
| Base XGBOOST | 0.90 | 0.95 | 0.92 |
| HyperTuned XGBOOST | 0.89 | 0.95 | 0.92 |
| HyperTuned KNN | 0.85 | 1.00 | 0.92 |
SmoteModelAttritionYes <- c("Base Decision Tree", "HyperTuned/PrePruned Decision Tree", 'PostPruned Base Decison Tree',
"Base XGBOOST", "HyperTuned XGBOOST",
"HyperTuned KNN")
Smote.RecallYes <- c(smote.dectree.baserecallYes, smote.dectree.hypertunedrecallYes,smote.postprunetree.baserecallYes,smote.xgboost.baserecallYes, smote.xgboost.hypertunedrecallYes,smote.knn.hypertunedrecallYes )
Smote.PrecisionYes <- c(smote.dectree.baseprecisionYes, smote.dectree.hypertunedprecisionYes,smote.postprunetree.baseprecisionYes,smote.xgboost.baseprecisionYes, smote.xgboost.hypertunedprecisionYes, smote.knn.hypertunedprecisionYes )
Smote.F1_ScoreYes<- c(smote.dectree.base_F1ScoreYes, smote.dectree.hypertuned_F1ScoreYes,smote.postprunetree.base_F1ScoreYes,smote.xgboost.base_F1ScoreYes, smote.xgboost.hypertuned_F1ScoreYes, smote.knn.hypertuned_F1ScoreYes )
AttritionYesResultsSmote <- data.frame(SmoteModelAttritionYes , Smote.RecallYes,Smote.PrecisionYes ,Smote.F1_ScoreYes)
AttritionYesResultsSmote
| SmoteModelAttritionYes | Smote.RecallYes | Smote.PrecisionYes | Smote.F1_ScoreYes |
|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> |
| Base Decision Tree | 0.71 | 0.26 | 0.38 |
| HyperTuned/PrePruned Decision Tree | 0.72 | 0.28 | 0.40 |
| PostPruned Base Decison Tree | 0.71 | 0.29 | 0.41 |
| Base XGBOOST | 0.71 | 0.29 | 0.41 |
| HyperTuned XGBOOST | 0.74 | 0.34 | 0.47 |
| HyperTuned KNN | 0.62 | 0.21 | 0.31 |
AttritionYesResults
| BaseModelAttritionYes | Yes.Recall | Yes.Precision | Yes.F1_Score |
|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> |
| Base Decision Tree | 0.42 | 0.50 | 0.46 |
| HyperTuned/PrePruned Decision Tree | 0.25 | 0.58 | 0.35 |
| PostPruned Base Decison Tree | 0.12 | 0.56 | 0.20 |
| Base XGBOOST | 0.44 | 0.65 | 0.52 |
| HyperTuned XGBOOST | 0.40 | 0.60 | 0.48 |
| HyperTuned KNN | 0.10 | 0.88 | 0.18 |
SmoteModelAttritionNo <- c("Base Decision Tree", "HyperTuned/PrePruned Decision Tree", 'PostPruned Base Decison Tree',
"Base XGBOOST", "HyperTuned XGBOOST",
"HyperTuned KNN")
Smote.RecallNo <- c(smote.dectree.baserecallNo, smote.dectree.hypertunedrecallNo,smote.postprunetree.baserecallNo,smote.xgboost.baserecallNo, smote.xgboost.hypertunedrecallNo,smote.knn.hypertunedrecallNo)
Smote.PrecisionNo <- c(smote.dectree.baseprecisionNo, smote.dectree.hypertunedprecisionNo,smote.postprunetree.baseprecisionNo,smote.xgboost.baseprecisionNo, smote.xgboost.hypertunedprecisionNo, smote.knn.hypertunedprecisionNo )
Smote.F1_ScoreNo<- c(smote.dectree.base_F1ScoreNo, smote.dectree.hypertuned_F1ScoreNo,smote.postprunetree.base_F1ScoreNo,smote.xgboost.base_F1ScoreNo, smote.xgboost.hypertuned_F1ScoreNo, smote.knn.hypertuned_F1ScoreNo )
AttritionNoResultsSmote <- data.frame(SmoteModelAttritionNo ,Smote.RecallNo,Smote.PrecisionNo ,Smote.F1_ScoreNo )